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Sonar - Decentralized Model Training Simulation (local)DISCLAIMER: This is a proof-of-concept implementation. It does not represent a remotely product ready implementation or follow proper conventions for security, convenience, or scalability. It is part of a broader proof-of-concept demonstrating the vision of the OpenMined project, its major moving parts, and how they might work together. Imports and Convenience Functions | import warnings
import numpy as np
import phe as paillier
from sonar.contracts import ModelRepository,Model
from syft.he.Paillier import KeyPair
from syft.nn.linear import LinearClassifier
import numpy as np
from sklearn.datasets import load_diabetes
def get_balance(account):
return repo.web3.fromWei(repo.web3.eth.getBalance(account),'ether')
warnings.filterwarnings('ignore') | _____no_output_____ | Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Setting up the Experiment | # for the purpose of the simulation, we're going to split our dataset up amongst
# the relevant simulated users
diabetes = load_diabetes()
y = diabetes.target
X = diabetes.data
validation = (X[0:42],y[0:42])
anonymous_diabetes_users = (X[42:],y[42:])
# we're also going to initialize the model trainer smart contract, which in the
# real world would already be on the blockchain (managing other contracts) before
# the simulation begins
# ATTENTION: copy paste the correct address (NOT THE DEFAULT SEEN HERE) from truffle migrate output.
repo = ModelRepository('0xf30068fb49616db7d5afb89862d6b40d11389327', ipfs_host='localhost', web3_host='localhost') # blockchain hosted model repository
# we're going to set aside 400 accounts for our 400 patients
# Let's go ahead and pair each data point with each patient's
# address so that we know we don't get them confused
patient_addresses = repo.web3.eth.accounts[1:40]
anonymous_diabetics = list(zip(patient_addresses,
anonymous_diabetes_users[0],
anonymous_diabetes_users[1]))
# we're going to set aside 1 account for Cure Diabetes Inc
cure_diabetes_inc = repo.web3.eth.accounts[0] | _____no_output_____ | Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Step 1: Cure Diabetes Inc Initializes a Model and Provides a Bounty | pubkey,prikey = KeyPair().generate(n_length=1024)
diabetes_classifier = LinearClassifier(desc="DiabetesClassifier",n_inputs=10,n_labels=1)
initial_error = diabetes_classifier.evaluate(validation[0],validation[1])
diabetes_classifier.encrypt(pubkey)
diabetes_model = Model(owner=cure_diabetes_inc,
syft_obj = diabetes_classifier,
bounty = 1,
initial_error = initial_error,
target_error = 10000
)
model_id = repo.submit_model(diabetes_model)
cure_diabetes_inc | _____no_output_____ | Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Step 2: An Anonymous Patient Downloads the Model and Improves It | model_id
model = repo[model_id]
diabetic_address,input_data,target_data = anonymous_diabetics[0]
repo[model_id].submit_gradient(diabetic_address,input_data,target_data) | _____no_output_____ | Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Step 3: Cure Diabetes Inc. Evaluates the Gradient | repo[model_id]
old_balance = get_balance(diabetic_address)
print(old_balance)
new_error = repo[model_id].evaluate_gradient(cure_diabetes_inc,repo[model_id][0],prikey,pubkey,validation[0],validation[1])
new_error
new_balance = get_balance(diabetic_address)
incentive = new_balance - old_balance
print(incentive) | 0.000840812917924814
| Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Step 4: Rinse and Repeat | model
for i,(addr, input, target) in enumerate(anonymous_diabetics):
try:
model = repo[model_id]
# patient is doing this
model.submit_gradient(addr,input,target)
# Cure Diabetes Inc does this
old_balance = get_balance(addr)
new_error = model.evaluate_gradient(cure_diabetes_inc,model[i+1],prikey,pubkey,validation[0],validation[1],alpha=2)
print("new error = "+str(new_error))
incentive = round(get_balance(addr) - old_balance,5)
print("incentive = "+str(incentive))
except:
"Connection Reset" | new error = 26580005
incentive = 0.00162
new error = 26639344
incentive = 0.00000
new error = 26536737
incentive = 0.00163
new error = 26546235
incentive = 0.00000
| Apache-2.0 | notebooks/Pre-Hydrogen-Demo.ipynb | jopasserat/PySonar |
Welcome to the [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) ColabTensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and [accelerate ML research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html). T2T is actively used and maintained by researchers and engineers within the [Google Brain team](https://research.google.com/teams/brain/) and a community of users. This colab shows you some datasets we have in T2T, how to download and use them, some models we have, how to download pre-trained models and use them, and how to create and train your own models. | #@title
# Copyright 2018 Google LLC.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Install deps
!pip install -q -U tensor2tensor
!pip install -q tensorflow matplotlib
# Imports we need.
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os
import collections
from tensor2tensor import models
from tensor2tensor import problems
from tensor2tensor.layers import common_layers
from tensor2tensor.utils import trainer_lib
from tensor2tensor.utils import t2t_model
from tensor2tensor.utils import registry
from tensor2tensor.utils import metrics
# Enable TF Eager execution
tfe = tf.contrib.eager
tfe.enable_eager_execution()
# Other setup
Modes = tf.estimator.ModeKeys
# Setup some directories
data_dir = os.path.expanduser("~/t2t/data")
tmp_dir = os.path.expanduser("~/t2t/tmp")
train_dir = os.path.expanduser("~/t2t/train")
checkpoint_dir = os.path.expanduser("~/t2t/checkpoints")
tf.gfile.MakeDirs(data_dir)
tf.gfile.MakeDirs(tmp_dir)
tf.gfile.MakeDirs(train_dir)
tf.gfile.MakeDirs(checkpoint_dir)
gs_data_dir = "gs://tensor2tensor-data"
gs_ckpt_dir = "gs://tensor2tensor-checkpoints/" | /home/jk/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
| MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
Download MNIST and inspect it | # A Problem is a dataset together with some fixed pre-processing.
# It could be a translation dataset with a specific tokenization,
# or an image dataset with a specific resolution.
#
# There are many problems available in Tensor2Tensor
problems.available()
# Fetch the MNIST problem
mnist_problem = problems.problem("image_mnist")
# The generate_data method of a problem will download data and process it into
# a standard format ready for training and evaluation.
mnist_problem.generate_data(data_dir, tmp_dir)
# Now let's see the training MNIST data as Tensors.
mnist_example = tfe.Iterator(mnist_problem.dataset(Modes.TRAIN, data_dir)).next()
image = mnist_example["inputs"]
label = mnist_example["targets"]
plt.imshow(image.numpy()[:, :, 0].astype(np.float32), cmap=plt.get_cmap('gray'))
print("Label: %d" % label.numpy()) | INFO:tensorflow:Reading data files from /home/jk/t2t/data/image_mnist-train*
INFO:tensorflow:partition: 0 num_data_files: 10
Label: 1
| MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
Translate from English to German with a pre-trained model | # Fetch the problem
ende_problem = problems.problem("translate_ende_wmt32k")
# Copy the vocab file locally so we can encode inputs and decode model outputs
# All vocabs are stored on GCS
vocab_name = "vocab.ende.32768"
vocab_file = os.path.join(gs_data_dir, vocab_name)
!gsutil cp {vocab_file} {data_dir}
# Get the encoders from the problem
encoders = ende_problem.feature_encoders(data_dir)
# Setup helper functions for encoding and decoding
def encode(input_str, output_str=None):
"""Input str to features dict, ready for inference"""
inputs = encoders["inputs"].encode(input_str) + [1] # add EOS id
batch_inputs = tf.reshape(inputs, [1, -1, 1]) # Make it 3D.
return {"inputs": batch_inputs}
def decode(integers):
"""List of ints to str"""
integers = list(np.squeeze(integers))
if 1 in integers:
integers = integers[:integers.index(1)]
return encoders["inputs"].decode(np.squeeze(integers))
# # Generate and view the data
# # This cell is commented out because WMT data generation can take hours
# ende_problem.generate_data(data_dir, tmp_dir)
# example = tfe.Iterator(ende_problem.dataset(Modes.TRAIN, data_dir)).next()
# inputs = [int(x) for x in example["inputs"].numpy()] # Cast to ints.
# targets = [int(x) for x in example["targets"].numpy()] # Cast to ints.
# # Example inputs as int-tensor.
# print("Inputs, encoded:")
# print(inputs)
# print("Inputs, decoded:")
# # Example inputs as a sentence.
# print(decode(inputs))
# # Example targets as int-tensor.
# print("Targets, encoded:")
# print(targets)
# # Example targets as a sentence.
# print("Targets, decoded:")
# print(decode(targets))
# There are many models available in Tensor2Tensor
registry.list_models()
# Create hparams and the model
model_name = "transformer"
hparams_set = "transformer_base"
hparams = trainer_lib.create_hparams(hparams_set, data_dir=data_dir, problem_name="translate_ende_wmt32k")
# NOTE: Only create the model once when restoring from a checkpoint; it's a
# Layer and so subsequent instantiations will have different variable scopes
# that will not match the checkpoint.
translate_model = registry.model(model_name)(hparams, Modes.EVAL)
# Copy the pretrained checkpoint locally
ckpt_name = "transformer_ende_test"
gs_ckpt = os.path.join(gs_ckpt_dir, ckpt_name)
!gsutil -q cp -R {gs_ckpt} {checkpoint_dir}
ckpt_path = tf.train.latest_checkpoint(os.path.join(checkpoint_dir, ckpt_name))
ckpt_path
# Restore and translate!
def translate(inputs):
encoded_inputs = encode(inputs)
with tfe.restore_variables_on_create(ckpt_path):
model_output = translate_model.infer(encoded_inputs)["outputs"]
return decode(model_output)
inputs = "The animal didn't cross the street because it was too tired"
outputs = translate(inputs)
print("Inputs: %s" % inputs)
print("Outputs: %s" % outputs) | INFO:tensorflow:Greedy Decoding
Inputs: The animal didn't cross the street because it was too tired
Outputs: Das Tier überquerte nicht die Straße, weil es zu müde war.
| MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
Attention Viz Utils | from tensor2tensor.visualization import attention
from tensor2tensor.data_generators import text_encoder
SIZE = 35
def encode_eval(input_str, output_str):
inputs = tf.reshape(encoders["inputs"].encode(input_str) + [1], [1, -1, 1, 1]) # Make it 3D.
outputs = tf.reshape(encoders["inputs"].encode(output_str) + [1], [1, -1, 1, 1]) # Make it 3D.
return {"inputs": inputs, "targets": outputs}
def get_att_mats():
enc_atts = []
dec_atts = []
encdec_atts = []
for i in range(hparams.num_hidden_layers):
enc_att = translate_model.attention_weights[
"transformer/body/encoder/layer_%i/self_attention/multihead_attention/dot_product_attention" % i][0]
dec_att = translate_model.attention_weights[
"transformer/body/decoder/layer_%i/self_attention/multihead_attention/dot_product_attention" % i][0]
encdec_att = translate_model.attention_weights[
"transformer/body/decoder/layer_%i/encdec_attention/multihead_attention/dot_product_attention" % i][0]
enc_atts.append(resize(enc_att))
dec_atts.append(resize(dec_att))
encdec_atts.append(resize(encdec_att))
return enc_atts, dec_atts, encdec_atts
def resize(np_mat):
# Sum across heads
np_mat = np_mat[:, :SIZE, :SIZE]
row_sums = np.sum(np_mat, axis=0)
# Normalize
layer_mat = np_mat / row_sums[np.newaxis, :]
lsh = layer_mat.shape
# Add extra dim for viz code to work.
layer_mat = np.reshape(layer_mat, (1, lsh[0], lsh[1], lsh[2]))
return layer_mat
def to_tokens(ids):
ids = np.squeeze(ids)
subtokenizer = hparams.problem_hparams.vocabulary['targets']
tokens = []
for _id in ids:
if _id == 0:
tokens.append('<PAD>')
elif _id == 1:
tokens.append('<EOS>')
elif _id == -1:
tokens.append('<NULL>')
else:
tokens.append(subtokenizer._subtoken_id_to_subtoken_string(_id))
return tokens
def call_html():
import IPython
display(IPython.core.display.HTML('''
<script src="/static/components/requirejs/require.js"></script>
<script>
requirejs.config({
paths: {
base: '/static/base',
"d3": "https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.8/d3.min",
jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',
},
});
</script>
''')) | _____no_output_____ | MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
Display Attention | # Convert inputs and outputs to subwords
inp_text = to_tokens(encoders["inputs"].encode(inputs))
out_text = to_tokens(encoders["inputs"].encode(outputs))
# Run eval to collect attention weights
example = encode_eval(inputs, outputs)
with tfe.restore_variables_on_create(tf.train.latest_checkpoint(checkpoint_dir)):
translate_model.set_mode(Modes.EVAL)
translate_model(example)
# Get normalized attention weights for each layer
enc_atts, dec_atts, encdec_atts = get_att_mats()
call_html()
attention.show(inp_text, out_text, enc_atts, dec_atts, encdec_atts) | INFO:tensorflow:Transforming feature 'inputs' with symbol_modality_33708_512.bottom
INFO:tensorflow:Transforming 'targets' with symbol_modality_33708_512.targets_bottom
INFO:tensorflow:Building model body
INFO:tensorflow:Transforming body output with symbol_modality_33708_512.top
| MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
Train a custom model on MNIST | # Create your own model
class MySimpleModel(t2t_model.T2TModel):
def body(self, features):
inputs = features["inputs"]
filters = self.hparams.hidden_size
h1 = tf.layers.conv2d(inputs, filters,
kernel_size=(5, 5), strides=(2, 2))
h2 = tf.layers.conv2d(tf.nn.relu(h1), filters,
kernel_size=(5, 5), strides=(2, 2))
return tf.layers.conv2d(tf.nn.relu(h2), filters,
kernel_size=(3, 3))
hparams = trainer_lib.create_hparams("basic_1", data_dir=data_dir, problem_name="image_mnist")
hparams.hidden_size = 64
model = MySimpleModel(hparams, Modes.TRAIN)
# Prepare for the training loop
# In Eager mode, opt.minimize must be passed a loss function wrapped with
# implicit_value_and_gradients
@tfe.implicit_value_and_gradients
def loss_fn(features):
_, losses = model(features)
return losses["training"]
# Setup the training data
BATCH_SIZE = 128
mnist_train_dataset = mnist_problem.dataset(Modes.TRAIN, data_dir)
mnist_train_dataset = mnist_train_dataset.repeat(None).batch(BATCH_SIZE)
optimizer = tf.train.AdamOptimizer()
# Train
NUM_STEPS = 500
for count, example in enumerate(tfe.Iterator(mnist_train_dataset)):
example["targets"] = tf.reshape(example["targets"], [BATCH_SIZE, 1, 1, 1]) # Make it 4D.
loss, gv = loss_fn(example)
optimizer.apply_gradients(gv)
if count % 50 == 0:
print("Step: %d, Loss: %.3f" % (count, loss.numpy()))
if count >= NUM_STEPS:
break
model.set_mode(Modes.EVAL)
mnist_eval_dataset = mnist_problem.dataset(Modes.EVAL, data_dir)
# Create eval metric accumulators for accuracy (ACC) and accuracy in
# top 5 (ACC_TOP5)
metrics_accum, metrics_result = metrics.create_eager_metrics(
[metrics.Metrics.ACC, metrics.Metrics.ACC_TOP5])
for count, example in enumerate(tfe.Iterator(mnist_eval_dataset)):
if count >= 200:
break
# Make the inputs and targets 4D
example["inputs"] = tf.reshape(example["inputs"], [1, 28, 28, 1])
example["targets"] = tf.reshape(example["targets"], [1, 1, 1, 1])
# Call the model
predictions, _ = model(example)
# Compute and accumulate metrics
metrics_accum(predictions, example["targets"])
# Print out the averaged metric values on the eval data
for name, val in metrics_result().items():
print("%s: %.2f" % (name, val)) | INFO:tensorflow:Reading data files from /content/t2t/data/image_mnist-dev*
INFO:tensorflow:partition: 0 num_data_files: 1
accuracy_top5: 0.99
accuracy: 0.97
| MIT | CS20_Tensorflow_for_Deep_learning_Research/Tensor2Tensor_Intro.ipynb | jungi21cc/DeepLearning |
ArcGIS Online の解析機能を使用する 使用するデータ* 栃木県のダム諸元表: https://www.geospatial.jp/ckan/dataset/09000-103 データを確認する | # pandas を使用して csv ファイルの読み込み、中身を表示する
import pandas as pd
dam_csv = pd.read_csv('https://www.geospatial.jp/ckan/dataset/d6a87e42-6e86-449e-9d76-1e40319bb99b/resource/b5d633c8-f2c8-4baa-88a9-fcf1872dcfcd/download/724522014tochiginodamsyogen04033.csv',encoding="SHIFT-JIS")
dam_csv | _____no_output_____ | Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
ArcGIS Online にログイン | # ArcGIS Online に開発者アカウントでサインインする
from arcgis.gis import GIS
import getpass
develoersUser = 'あなたのユーザー名'
develoersPass = getpass.getpass('ユーザー['+ develoersUser + ']のパスワード=')
gis = GIS("http://"+ develoersUser +".maps.arcgis.com/",develoersUser,develoersPass)
user = gis.users.get(develoersUser)
user | ユーザー[ejpythondev]のパスワード=········
| Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
ArcGIS Online にホスト フィーチャ サービスを公開する | # ArcGIS Online に CSV ファイルをアイテムとして追加する
csv_file = 'https://www.geospatial.jp/ckan/dataset/d6a87e42-6e86-449e-9d76-1e40319bb99b/resource/b5d633c8-f2c8-4baa-88a9-fcf1872dcfcd/download/724522014tochiginodamsyogen04033.csv'
csv_item = gis.content.add({}, csv_file)
display(csv_item)
# CSV にある緯度経度の情報を使用して、追加したアイテムからホスト フィーチャ サービス（ダムのポイント）を公開する
csv_lyr = csv_item.publish({'name':'dam','locationType':'coordinates', 'latitudeFieldName':'緯度', 'longitudeFieldName':'経度'})
display(csv_lyr)
# マップにホスト フィーチャ サービス追加して表示する
map = gis.map('栃木県')
map.add_layer(csv_lyr)
map | _____no_output_____ | Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
ArcGIS Online の集水域解析を実行する | # ダムのポイントのホスト フィーチャ サービスを引数にして集水域の作成ツール（create_watersheds）を実行する
from arcgis.features import analysis
watershedsResult = analysis.create_watersheds(csv_lyr, output_name='watersheds_result')
watershedsResult
# 解析結果の集水域ポリゴンをマップに追加して表示する
map.add_layer(watershedsResult) | _____no_output_____ | Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
ArcGIS Online の下流解析を実行する | # 集水域の解析結果で出力された調整された入力ポイントを引数にして下流解析ツール（trace_downstream）を実行する
input_layer = watershedsResult.layers[0]
downstreamResult = analysis.trace_downstream(input_layer, output_name='downstream_result')
downstreamResult
# 解析結果の河川ラインをマップに追加して表示する
map.add_layer(downstreamResult) | _____no_output_____ | Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
Web マップとして保存する | # Web マップのタイトルなどを定義する
webMap_properties = {'title':'栃木県のダム・河川',
'snippet':'Python API で作成した栃木県のダム・河川 Web マップ',
'tags':'栃木県, ダム, 河川',
'extent':downstreamResult.extent
}
# Web マップを保存する
webMap = map.save(item_properties=webMap_properties)
webMap | _____no_output_____ | Apache-2.0 | samples/5.analysis.ipynb | EsriJapan/arcgis-samples-python-api |
Some testing and analysis of the new `Snapshot` implementation | from __future__ import print_function
import numpy as np
import openpathsampling as paths
import openpathsampling.engines.features as features | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Function to show the generated source code | from IPython.display import Markdown
def code_to_md(snapshot_class):
md = '```py\n'
for f, s in snapshot_class.__features__.debug.items():
if s is not None:
md += s
else:
md += 'def ' + f + '(...):\n # user defined\n pass'
md += '\n\n'
md += '```'
return md | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Check generated source code Generate simple Snapshot without any features using factory | EmptySnap = paths.engines.snapshot.SnapshotFactory('no', [], 'Empty', use_lazy_reversed=False) | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Generate Snapshot with overridden `.copy` method. | @features.base.attach_features([
features.velocities,
features.coordinates,
features.box_vectors,
features.topology
])
class A(paths.BaseSnapshot):
def copy(self):
return 'copy' | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Check that subclassing with overridden copy needs more overriding. | #! lazy
# lazy because of some issue with Py3k comparing strings
try:
@features.base.attach_features([
])
class B(A):
pass
except RuntimeWarning as e:
print(e)
else:
raise RuntimeError('Should have raised a RUNTIME warning')
a = A()
assert(a.copy() == 'copy')
# NBVAL_IGNORE_OUTPUT
Markdown(code_to_md(A))
# NBVAL_IGNORE_OUTPUT
Markdown(code_to_md(EmptySnap))
SuperSnap = paths.engines.snapshot.SnapshotFactory(
'my', [
paths.engines.features.coordinates,
paths.engines.features.box_vectors,
paths.engines.features.velocities
], 'No desc', use_lazy_reversed=False)
# NBVAL_IGNORE_OUTPUT
Markdown(code_to_md(SuperSnap))
MegaSnap = paths.engines.snapshot.SnapshotFactory(
'mega', [
paths.engines.features.statics,
paths.engines.features.kinetics,
paths.engines.features.engine
], 'Long desc', use_lazy_reversed=False)
# NBVAL_IGNORE_OUTPUT
Markdown(code_to_md(MegaSnap)) | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Test subclassing | @features.base.attach_features([
])
class HyperSnap(MegaSnap):
pass | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Test subclassing with redundant features (should work / be ignored) | @features.base.attach_features([
paths.engines.features.statics,
])
class HyperSnap(MegaSnap):
pass | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Test subclassing with conflicting features (should not work) | try:
@features.base.attach_features([
paths.engines.features.statics,
paths.engines.features.coordinates
])
class HyperSnap(MegaSnap):
pass
except RuntimeWarning as e:
print(e)
else:
raise RuntimeError('Should have raised a RUNTIME warning')
# NBVAL_IGNORE_OUTPUT
Markdown(code_to_md(paths.engines.openmm.MDSnapshot)) | _____no_output_____ | MIT | examples/tests/test_snapshot.ipynb | bdice/openpathsampling |
Find the Tents_Combinatorial Optimization course, FEE CTU in Prague. Created by [Industrial Informatics Department](http://industrialinformatics.fel.cvut.cz)._The problem was taken from https://www.brainbashers.com/tents.asp ; there, you can try to solve some examples manually. TaskFind all of the hidden tents in the forest grid.You know that:- Each tent is attached to one tree (so there are as many tents as there are trees).- A tent can only be found horizontally or vertically adjacent to a tree.- Tents are never adjacent to each other, neither vertically, horizontally, nor diagonally.- A tree might be next to two tents but is only connected to one.You are also given two vectors indicating how many tents are in each respective row or column of the forest grid. InputYou are given a positive integer $n \geq 2$, representing the size of the forest grid (assume it is a square of size $(n \times n$). You are also given vectors $\mathbf r = (r_1, \dots, r_n)$ and $\mathbf c = (c_1, \dots, c_n)$ representing the numbers of the tents in the rows and columns of the forest grid. Finally, you are given a list of coordinates of the trees $((x_1, y_1), \dots, (x_k, y_k))$. | # 2x2 - Extra small (for debugging)
n1 = 3
r1 = (1, 1, 0)
c1 = (1, 0, 1)
trees1 = [(1,1), (3,2)]
# 8x8 - Medium
n2 = 8
r2 = (3, 1, 1, 2, 0, 2, 0, 3)
c2 = (2, 1, 2, 2 ,1, 1 ,2 ,1)
trees2 = [(2, 1), (5, 1), (6, 1),
(1, 2),
(3, 3),
(3, 4), (6, 4),
(4, 5), (6, 5),
(8, 7),
(2, 8), (4, 8)]
# Weekly special
n3 = 20
r3 = (7, 2, 3, 4, 3, 5, 4, 4, 4, 4, 3, 6, 3, 6, 2, 3, 6, 3, 3, 5)
c3 = (6, 4, 3, 5, 4, 4, 4, 3, 5, 3, 4, 3, 4, 4, 6, 3 ,4, 3, 6, 2)
trees3 = [(3, 1), (4, 1), (8, 1), (13, 1), (15, 1),
(1, 2), (9, 2), (18, 2), (19, 2),
(5, 3), (12, 3), (15, 3),
(2, 4), (4, 4), (9, 4), (17, 4),
(6, 5), (10, 5), (13, 5), (17, 5), (20, 5),
(1, 6), (7, 6), (10, 6), (12, 6), (16, 6),
(20, 7),
(1, 8), (4, 8), (5, 8), (11, 8), (13, 8), (14, 8), (19, 8),
(4, 9), (6, 9), (9, 9), (15, 9), (17, 9),
(8, 10), (17, 10), (19, 10),
(12, 11),
(5, 12), (7, 12), (14, 12), (16, 12),
(1, 13), (2, 13), (6, 13), (19, 13),
(11, 14), (14, 14), (20, 14),
(3, 15), (5, 15), (6, 15), (8, 15), (13, 15), (20, 15),
(2, 16), (3, 16), (10, 16),
(8, 17), (11, 17), (14, 17), (15, 17),
(2, 18), (6, 18), (9, 18), (12, 18), (13, 18), (18, 18),
(2, 19), (7, 19), (15, 19), (17, 19), (20, 19),
(5, 20), (10, 20)] | _____no_output_____ | MIT | cv06/forest.ipynb | LukasForst/KO |
OutputYou should find the coordinates $(x_i, y_i), i \in \{1,\dots,k\}$, of the individual tents. Model | from gurobipy import *
from itertools import product as cartesian
def optimize(n, r, c, trees):
m = Model()
# n+2 -> extend the board such as we don't need check borders
# this is really nice hack, disable all variables with uper bound 0 and
# then allow them only in tree neighborhood -> we don't need second matrix
X = m.addVars(n+2, n+2, vtype=GRB.BINARY, ub=0)
# set sums per rows, iterate only through valid board (not extended)
for i, val in enumerate(r, start=1):
m.addConstr(sum(X[j, i] for j in range(1, n+1)) == val)
# sums per columns
for j, val in enumerate(c, start=1):
m.addConstr(sum(X[j, i] for i in range(1, n+1)) == val)
# no other tents near one tent
tents_neighboorhood = {(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)}
# sum max of neighberhood + 1
M = 9
for i in range(1, n+1):
for j in range(1, n+1):
# if there's a tent, there can't be another one around it
m.addConstr(M * (1 - X[i, j]) >= sum(X[i+ii, j+jj] for ii, jj in tents_neighboorhood))
# as we extended board to n+2, we need to know indicies that are ouside of the board
outer_frame = set(cartesian(range(n+2), range(n+2))) - set(cartesian(range(1,n+1), range(1,n+1)))
# tents can be only in these incidies
allowed_tent_indicies = {(1,0), (-1,0), (0,1), (0,-1)}
for i, j in trees:
allowed_neighberhood = [(i+ii, j+jj) for (ii,jj) in allowed_tent_indicies]
# allow to place tent only if there's tree
for idx in allowed_neighberhood:
# if index is inside the board, allow to place tent
if idx not in outer_frame:
# thanks Prokop
X[idx].ub = 1
# there must be at least one tent in the neighberhood
m.addConstr(sum(X[idx] for idx in allowed_neighberhood) >= 1)
m.optimize()
return [coords for coords, var in X.items() if var.x > 0] | _____no_output_____ | MIT | cv06/forest.ipynb | LukasForst/KO |
Visualization | import matplotlib.pyplot as plt
import numpy as np
def visualize(n, trees, tents, r, c):
grid = [["." for _ in range(n+2)] for _ in range(n+2)]
for t_x, t_y in tents:
grid[t_y][t_x] = "X"
for t_x, t_y in trees:
grid[t_y][t_x] = "T"
print(" ", end="")
for c_cur in c:
print(c_cur, end=" ")
print()
for y in range(1, n+1):
print(r[y-1], end=" ")
for x in range(1, n+1):
print(grid[y][x], end=" ")
print()
tents1 = optimize(n1, r1, c1, trees1)
visualize(n1, trees1, tents1, r1, c1)
tents2 = optimize(n2, r2, c2, trees2)
visualize(n2, trees2, tents2, r2, c2)
tents3 = optimize(n3, r3, c3, trees3)
visualize(n3, trees3, tents3, r3, c3) | Gurobi Optimizer version 9.0.1 build v9.0.1rc0 (mac64)
Optimize a model with 520 rows, 484 columns and 4720 nonzeros
Model fingerprint: 0xcc059f6b
Variable types: 0 continuous, 484 integer (484 binary)
Coefficient statistics:
Matrix range [1e+00, 9e+00]
Objective range [0e+00, 0e+00]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 9e+00]
Presolve removed 198 rows and 283 columns
Presolve time: 0.01s
Presolved: 322 rows, 201 columns, 1586 nonzeros
Variable types: 0 continuous, 201 integer (201 binary)
Root relaxation: objective 0.000000e+00, 600 iterations, 0.03 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 0.00000 0 97 - 0.00000 - - 0s
H 0 0 0.0000000 0.00000 0.00% - 0s
0 0 - 0 0.00000 0.00000 0.00% - 0s
Cutting planes:
Gomory: 27
Cover: 35
Clique: 3
MIR: 8
StrongCG: 2
Zero half: 16
RLT: 19
Explored 1 nodes (1257 simplex iterations) in 0.12 seconds
Thread count was 12 (of 12 available processors)
Solution count 1: 0
Optimal solution found (tolerance 1.00e-04)
Best objective 0.000000000000e+00, best bound 0.000000000000e+00, gap 0.0000%
6 4 3 5 4 4 4 3 5 3 4 3 4 4 6 3 4 3 6 2
7 . X T T X . X T X . . . T X T X . X . .
2 T . . . . . . . T . . X . . . . . T T X
3 X . . X T . . . . . . T . X T . . . . .
4 . T . T . X . X T X . . . . . X T . . .
3 . X . X . T . . . T . . T . . . T . X T
5 T . . . . . T . X T X T X . X T X . . .
4 X . . . X . X . . . . . . . . . . . X T
4 T . X T T . . . X . T . T T X . X . T .
4 X . . T . T . . T . X . X . T . T . X .
4 . . . X . X . T X . . . . . X . T . T .
3 . . . . . . . . . . X T . . . . X . X .
6 . X . X T X T X . . . . X T X T . . . .
3 T T . . . T . . . . X . . . . . . X T X
6 X . X . . X . X . . T . X T X . . . . T
2 . . T . T T . T . X . . T . . . . . X T
3 X T T . X . . . . T . . . X . . . . . .
6 . . X . . . X T X . T X . T T X . X . .
3 X T . . X T . . T . . T T X . . . T . .
3 . T . . . . T . . X . X . . T . T . X T
5 . X . X T . X . . T . . . . X . X . . .
| MIT | cv06/forest.ipynb | LukasForst/KO |
GradientBoostingRegressor with Normalize
Required Packages | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as se
from sklearn.preprocessing import Normalizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
warnings.filterwarnings('ignore') | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
InitializationFilepath of CSV file | #filepath
file_path = "" | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
List of features which are required for model training . | #x_values
features=[] | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Target feature for prediction. | #y_value
target = '' | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Data FetchingPandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry. | df=pd.read_csv(file_path)
df.head() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Feature SelectionsIt is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.We will assign all the required input features to X and target/outcome to Y. | X = df[features]
Y = df[target] | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Data PreprocessingSince the majority of the machine learning models in the Sklearn library doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippet have functions, which removes the null value if any exists. And convert the string classes data in the datasets by encoding them to integer classes. | def NullClearner(df):
if(isinstance(df, pd.Series) and (df.dtype in ["float64","int64"])):
df.fillna(df.mean(),inplace=True)
return df
elif(isinstance(df, pd.Series)):
df.fillna(df.mode()[0],inplace=True)
return df
else:return df
def EncodeX(df):
return pd.get_dummies(df) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Calling preprocessing functions on the feature and target set. | x=X.columns.to_list()
for i in x:
X[i]=NullClearner(X[i])
X=EncodeX(X)
Y=NullClearner(Y)
X.head() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Correlation MapIn order to check the correlation between the features, we will plot a correlation matrix. It is effective in summarizing a large amount of data where the goal is to see patterns. | f,ax = plt.subplots(figsize=(18, 18))
matrix = np.triu(X.corr())
se.heatmap(X.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax, mask=matrix)
plt.show() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Data SplittingThe train-test split is a procedure for evaluating the performance of an algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of the model on new data. | X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 123)#performing datasplitting | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Data RescalingNormalizer normalizes samples (rows) individually to unit norm.Each sample with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one.We will fit an object of Normalizer to train data then transform the same data via fit_transform(X_train) method, following which we will transform test data via transform(X_test) method. | normalizer = Normalizer()
X_train = normalizer.fit_transform(X_train)
X_test = normalizer.transform(X_test) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
ModelGradient Boosting builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. Model Tuning Parameters 1. loss : {‘ls’, ‘lad’, ‘huber’, ‘quantile’}, default=’ls’> Loss function to be optimized. ‘ls’ refers to least squares regression. ‘lad’ (least absolute deviation) is a highly robust loss function solely based on order information of the input variables. ‘huber’ is a combination of the two. ‘quantile’ allows quantile regression (use `alpha` to specify the quantile). 2. learning_ratefloat, default=0.1> Learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators. 3. n_estimators : int, default=100> The number of trees in the forest. 4. criterion : {‘friedman_mse’, ‘mse’, ‘mae’}, default=’friedman_mse’> The function to measure the quality of a split. Supported criteria are ‘friedman_mse’ for the mean squared error with improvement score by Friedman, ‘mse’ for mean squared error, and ‘mae’ for the mean absolute error. The default value of ‘friedman_mse’ is generally the best as it can provide a better approximation in some cases. 5. max_depth : int, default=3> The maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. 6. max_features : {‘auto’, ‘sqrt’, ‘log2’}, int or float, default=None> The number of features to consider when looking for the best split: 7. random_state : int, RandomState instance or None, default=None> Controls both the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True) and the sampling of the features to consider when looking for the best split at each node (if `max_features < n_features`). 8. verbose : int, default=0> Controls the verbosity when fitting and predicting. 9. n_iter_no_change : int, default=None> n_iter_no_change is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside validation_fraction size of the training data as validation and terminate training when validation score is not improving in all of the previous n_iter_no_change numbers of iterations. The split is stratified. 10. tol : float, default=1e-4> Tolerance for the early stopping. When the loss is not improving by at least tol for n_iter_no_change iterations (if set to a number), the training stops. | # Build Model here
model = GradientBoostingRegressor(random_state = 123)
model.fit(X_train, y_train) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Model AccuracyWe will use the trained model to make a prediction on the test set.Then use the predicted value for measuring the accuracy of our model.> **score**: The **score** function returns the coefficient of determination R2 of the prediction. | print("Accuracy score {:.2f} %\n".format(model.score(X_test,y_test)*100)) | Accuracy score 93.80 %
| Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
> **r2_score**: The **r2_score** function computes the percentage variablility explained by our model, either the fraction or the count of correct predictions. > **mae**: The **mean abosolute error** function calculates the amount of total error(absolute average distance between the real data and the predicted data) by our model. > **mse**: The **mean squared error** function squares the error(penalizes the model for large errors) by our model. | y_pred=model.predict(X_test)
print("R2 Score: {:.2f} %".format(r2_score(y_test,y_pred)*100))
print("Mean Absolute Error {:.2f}".format(mean_absolute_error(y_test,y_pred)))
print("Mean Squared Error {:.2f}".format(mean_squared_error(y_test,y_pred))) | R2 Score: 93.80 %
Mean Absolute Error 3.24
Mean Squared Error 17.94
| Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Feature ImportancesThe Feature importance refers to techniques that assign a score to features based on how useful they are for making the prediction. | plt.figure(figsize=(8,6))
n_features = len(X.columns)
plt.barh(range(n_features), model.feature_importances_, align='center')
plt.yticks(np.arange(n_features), X.columns)
plt.xlabel("Feature importance")
plt.ylabel("Feature")
plt.ylim(-1, n_features) | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Prediction PlotFirst, we make use of a plot to plot the actual observations, with x_train on the x-axis and y_train on the y-axis.For the regression line, we will use x_train on the x-axis and then the predictions of the x_train observations on the y-axis. | plt.figure(figsize=(14,10))
plt.plot(range(20),y_test[0:20], color = "blue")
plt.plot(range(20),model.predict(X_test[0:20]), color = "red")
plt.legend(["Actual","prediction"])
plt.title("Predicted vs True Value")
plt.xlabel("Record number")
plt.ylabel(target)
plt.show() | _____no_output_____ | Apache-2.0 | Regression/Gradient Boosting Machine/GradientBoostingRegressor_Normalize.ipynb | devVipin01/ds-seed |
Gene Expression Simple DemoThis shows how to query BgeeDb gene expression data ingested in Monarch | ## Create an ontology factory in order to fetch Uberon
from ontobio.ontol_factory import OntologyFactory
ofactory = OntologyFactory()
ont = ofactory.create("uberon")
## Create a sub-ontology that excludes all relations other than is-a and part-of
subont = ont.subontology(relations=['subClassOf', 'BFO:0000050'])
## Create an association factory to get mouse gene-expression associations (sourced from bgeedb)
from ontobio.assoc_factory import AssociationSetFactory
afactory = AssociationSetFactory()
aset = afactory.create(ontology=subont, subject_category='gene', object_category='anatomy', taxon='NCBITaxon:10090')
# show first 5
["{} '{}'".format(g, aset.label(g)) for g in aset.subjects[:5]]
# fetch uberon term
[liver] = ont.search('liver')
liver
liver_genes = aset.query([liver])
["{} '{}'".format(g, aset.label(g)) for g in liver_genes]
## NOTE: we currently lack rank scores, see https://github.com/monarch-initiative/monarch-app/issues/1271
## For now let's do something naive
def specificity_score(g, t):
"""
Naive specificity score - penalize for every expression *not* in desired term, e.g. liver
"""
anns = aset.annotations(g)
nonspecific = [a for a in anns if t!=a and t not in subont.ancestors(a) and a not in subont.ancestors(g)]
return 1/(len(nonspecific)+1)
## Tuples of (gene_id, gene_symbol, score)
gscores = [(g,aset.label(g),specificity_score(g,liver)) for g in liver_genes]
gscores
sorted(gscores, key=lambda x: -x[2])
only_in_liver = [x for x in gscores if x[2] == 1.0]
only_in_liver
## get phenotype associations
mp = ofactory.create("mp")
pheno_aset = afactory.create(ontology=mp, subject_category='gene', object_category='phenotype', taxon='NCBITaxon:10090')
## Show phenotype anns for all genes in liver
for g in liver_genes:
anns = pheno_aset.annotations(g)
print("{} {} {}".format(g,aset.label(g), [(a,mp.label(a)) for a in anns]))
| MGI:95590 Ftl2-ps []
MGI:1098641 Wasf2 [('MP:0002188', 'small heart'), ('MP:0020329', 'decreased capillary density'), ('MP:0011091', 'prenatal lethality, complete penetrance'), ('HP:0002170', None), ('HP:0000969', None), ('MP:0003984', 'embryonic growth retardation'), ('MP:0000260', 'abnormal angiogenesis'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0001722', 'pale yolk sac'), ('MP:0000295', 'trabecula carnea hypoplasia'), ('MP:0008803', 'abnormal placental labyrinth vasculature morphology'), ('GO:0001667PHENOTYPE', None), ('HP:0025016', None), ('MP:0000822', 'abnormal brain ventricle morphology'), ('GO:0006928PHENOTYPE', None), ('MP:0003974', 'abnormal endocardium morphology'), ('GO:0001525PHENOTYPE', None), ('MP:0004251', 'failure of heart looping')]
MGI:3644452 Gm9083 []
MGI:3651858 Gm11337 []
MGI:3702318 Gm11989 []
MGI:1916396 Gsdmd [('MP:0011073', 'abnormal macrophage apoptosis')]
MGI:2443767 Aaas [('MP:0005379', 'endocrine/exocrine gland phenotype'), ('MP:0003631', 'nervous system phenotype'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0001417', 'decreased exploration in new environment'), ('MP:0005381', 'digestive/alimentary phenotype'), ('GO:0007612PHENOTYPE', None)]
MGI:2137698 Ugt1a6a []
MGI:1351659 Abcg5 [('HP:0001882', None), ('HP:0003010', None), ('HP:0003251', None), ('HP:0001638', None), ('HP:0004446', None), ('HP:0011875', None), ('MP:0002413', 'abnormal megakaryocyte progenitor cell morphology'), ('HP:0003540', None), ('MP:0006298', 'abnormal platelet activation'), ('HP:0011877', None), ('HP:0011273', None), ('HP:0001878', None), ('HP:0002155', None), ('HP:0008669', None), ('HP:0005513', None), ('HP:0001939', None), ('MP:0001265', 'decreased body size'), ('HP:0002240', None), ('HP:0003146', None)]
MGI:3705426 Rpl21-ps15 []
MGI:1914745 Tmem167b []
MGI:1347249 Psg16 []
MGI:3818630 Sco2 [('GO:0022904PHENOTYPE', None), ('HP:0001324', None), ('HP:0025321', None), ('MP:0010956', 'abnormal mitochondrial ATP synthesis coupled electron transport'), ('HP:0010836', None), ('GO:0003012PHENOTYPE', None), ('MP:0001392', 'abnormal locomotor behavior'), ('GO:0001701PHENOTYPE', None), ('MP:0011095', 'embryonic lethality between implantation and placentation, complete penetrance')]
MGI:1919235 Acad10 []
MGI:3780170 Gm2000 []
MGI:1343095 Emc8 [('MP:0010024', 'increased total body fat amount')]
MGI:5454530 Gm24753 []
MGI:3649027 Gm7117 []
MGI:5454475 Gm24698 []
MGI:4415000 Gm16580 []
MGI:3646089 Gm7803 []
MGI:97987 Rnu3b3 []
MGI:1353433 Timm8a1 []
MGI:3649865 Rpl23a-ps14 []
MGI:106686 Pon3 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0004921', 'decreased placenta weight'), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('MP:0003674', 'oxidative stress')]
MGI:88216 Btk [('MP:0001844', 'autoimmune response'), ('HP:0004325', None), ('HP:0006270', None), ('GO:0007249PHENOTYPE', None), ('MP:0005093', 'decreased B cell proliferation'), ('MP:0008186', 'increased pro-B cell number'), ('MP:0008203', 'absent B-1a cells'), ('MP:0002401', 'abnormal lymphopoiesis'), ('HP:0011990', None), ('HP:0003496', None), ('HP:0002850', None), ('HP:0004313', None), ('MP:0002491', 'decreased IgD level'), ('MP:0002451', 'abnormal macrophage physiology'), ('HP:0010978', None), ('MP:0005153', 'abnormal B cell proliferation'), ('MP:0009339', 'decreased splenocyte number'), ('MP:0009788', 'increased susceptibility to bacterial infection induced morbidity/mortality'), ('HP:0010976', None), ('GO:0030889PHENOTYPE', None), ('MP:0005387', 'immune system phenotype'), ('HP:0001881', None), ('HP:0003212', None), ('MP:0008211', 'decreased mature B cell number')]
MGI:3704359 Gm9803 []
MGI:3780980 Gm2810 []
MGI:2140962 Ugt2b34 []
MGI:2444981 Phldb2 []
MGI:3644778 Gm8738 []
MGI:88054 Apoc2 [('MP:0003975', 'increased circulating VLDL triglyceride level'), ('MP:0000180', 'abnormal circulating cholesterol level'), ('HP:0003233', None)]
MGI:3649201 Gm12396 []
MGI:5451834 Gm22057 []
MGI:1861354 Apbb1ip []
MGI:5804868 Gm45753 []
MGI:891967 Serpina1e []
MGI:2138853 AI182371 []
MGI:3041196 Fam198a []
MGI:1913761 Chtop [('HP:0010442', None)]
MGI:3783208 Gm15766 []
MGI:2385276 Kctd15 [('MP:0011940', 'decreased food intake'), ('MP:0013294', 'prenatal lethality prior to heart atrial septation'), ('HP:0010683', None)]
MGI:1927868 Pex14 [('MP:0011091', 'prenatal lethality, complete penetrance')]
MGI:1888526 Xpo4 []
MGI:5455017 Gm25240 []
MGI:1913534 Gkn2 [('HP:0001640', None)]
MGI:1930008 Ghrl [('MP:0005379', 'endocrine/exocrine gland phenotype'), ('MP:0005452', 'abnormal adipose tissue amount'), ('MP:0005381', 'digestive/alimentary phenotype'), ('MP:0005560', 'decreased circulating glucose level'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0005292', 'improved glucose tolerance')]
MGI:2385008 Ggact []
MGI:1921821 Kcnk16 []
MGI:96611 Itgb2 [('HP:0005404', None), ('MP:0001194', 'dermatitis'), ('HP:0003330', None), ('MP:0003156', 'abnormal leukocyte migration'), ('HP:0000938', None), ('MP:0001874', 'acanthosis'), ('HP:0000962', None), ('MP:0000702', 'enlarged lymph nodes'), ('GO:0045123PHENOTYPE', None), ('HP:0000509', None), ('HP:0200042', None), ('MP:0008623', 'increased circulating interleukin-3 level'), ('HP:0002732', None), ('HP:0011990', None), ('MP:0001222', 'epidermal hyperplasia'), ('HP:0003496', None), ('MP:0005094', 'abnormal T cell proliferation'), ('MP:0001246', 'mixed cellular infiltration to dermis'), ('MP:0002356', 'abnormal spleen red pulp morphology'), ('HP:0010987', None), ('MP:0008566', 'increased interferon-gamma secretion'), ('HP:0001744', None), ('MP:0005559', 'increased circulating glucose level'), ('HP:0001974', None), ('MP:0008126', 'increased dendritic cell number'), ('MP:0003434', 'decreased susceptibility to induced choroidal neovascularization'), ('MP:0000321', 'increased bone marrow cell number'), ('MP:0002606', 'increased basophil cell number'), ('MP:0000219', 'increased neutrophil cell number'), ('MP:0001191', 'abnormal skin condition'), ('HP:0012311', None), ('HP:0011840', None), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0000939', None), ('MP:0020137', 'decreased bone mineralization'), ('MP:0008567', 'decreased interferon-gamma secretion'), ('HP:0003765', None), ('MP:0002411', 'decreased susceptibility to bacterial infection'), ('HP:0001036', None), ('MP:0003813', 'abnormal hair follicle dermal papilla morphology'), ('HP:0001880', None), ('MP:0001186', 'pigmentation phenotype'), ('HP:0100828', None)]
MGI:894286 P4ha2 []
MGI:5455293 Gm25516 []
MGI:1922466 Cep128 []
MGI:3645079 Gm16470 []
MGI:3645628 Gm8019 []
MGI:3647773 Gm6498 []
MGI:3819557 Snord83b []
MGI:1914709 Nvl []
MGI:4937849 Gm17022 []
MGI:3583955 Rdh16f2 []
MGI:2138968 Clp1 [('MP:0002083', 'premature death'), ('MP:0003631', 'nervous system phenotype'), ('HP:0010831', None), ('MP:0008412', 'increased cellular sensitivity to oxidative stress'), ('HP:0003307', None), ('HP:0003202', None), ('MP:0002175', 'decreased brain weight'), ('MP:0003203', 'increased neuron apoptosis'), ('HP:0003323', None), ('HP:0001322', None), ('HP:0001251', None), ('MP:0012055', 'abnormal phrenic nerve innervation pattern to diaphragm'), ('MP:0005498', 'hyporesponsive to tactile stimuli'), ('MP:0000819', 'abnormal olfactory bulb morphology'), ('HP:0002398', None), ('MP:0003718', 'maternal effect'), ('HP:0011017', None), ('MP:0011400', 'lethality, complete penetrance'), ('MP:0001053', 'abnormal neuromuscular synapse morphology'), ('MP:0011087', 'neonatal lethality, complete penetrance')]
MGI:5453898 Gm24121 []
MGI:1915442 Leprotl1 [('MP:0002953', 'thick ventricular wall'), ('HP:0010679', None)]
MGI:3779824 Gm8941 []
MGI:3650622 Gm12338 []
MGI:2444508 Fitm2 [('MP:0002981', 'increased liver weight'), ('HP:0004324', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('HP:0000842', None), ('MP:0005386', 'behavior/neurological phenotype'), ('HP:0011014', None), ('MP:0013294', 'prenatal lethality prior to heart atrial septation'), ('HP:0007703', None), ('MP:0009116', 'abnormal brown fat cell morphology'), ('MP:0009124', 'increased brown fat cell lipid droplet size'), ('HP:0000855', None), ('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0005378', 'growth/size/body region phenotype')]
MGI:5455452 Gm25675 []
MGI:5454373 Gm24596 []
MGI:1096324 Lst1 [('HP:0000035', None), ('HP:0011001', None), ('HP:0001640', None), ('MP:0009791', 'increased susceptibility to viral infection induced morbidity/mortality'), ('HP:0008734', None), ('MP:0010123', 'increased bone mineral content')]
MGI:2159614 Mia2 [('HP:0040006', None), ('GO:0070328PHENOTYPE', None), ('HP:0003233', None), ('MP:0005389', 'reproductive system phenotype')]
MGI:1918956 Slc46a3 [('MP:0011275', 'abnormal behavioral response to light')]
MGI:3649696 Rps8-ps2 []
MGI:3644876 Rps2-ps6 []
MGI:5453824 Gm24047 []
MGI:3649769 Gm12355 []
MGI:2445040 Tyw3 []
MGI:5452129 Gm22352 []
MGI:1196423 Onecut1 [('HP:0001508', None), ('MP:0004201', 'fetal growth retardation'), ('HP:0100732', None), ('MP:0005559', 'increased circulating glucose level'), ('MP:0009181', 'decreased pancreatic delta cell number'), ('MP:0005379', 'endocrine/exocrine gland phenotype'), ('MP:0013221', 'pancreatic acinar-to-ductal metaplasia'), ('MP:0009164', 'exocrine pancreas atrophy'), ('HP:0006274', None), ('MP:0012242', 'abnormal hepatoblast differentiation'), ('GO:0048536PHENOTYPE', None), ('HP:0012090', None), ('HP:0005213', None), ('MP:0009254', 'disorganized pancreatic islets'), ('MP:0011932', 'abnormal endocrine pancreas development'), ('HP:0040216', None), ('MP:0009143', 'abnormal pancreatic duct morphology'), ('MP:0009178', 'absent pancreatic alpha cells'), ('GO:0031016PHENOTYPE', None), ('MP:0009145', 'abnormal pancreatic acinus morphology')]
MGI:97620 Plg [('GO:0042246PHENOTYPE', None), ('MP:0002083', 'premature death'), ('HP:0002088', None), ('HP:0001395', None), ('MP:0003305', 'proctitis'), ('HP:0004325', None), ('HP:0002577', None), ('HP:0000105', None), ('HP:0002035', None), ('MP:0005602', 'decreased angiogenesis'), ('MP:0012331', 'increased circulating fibrinogen level'), ('MP:0010211', 'abnormal acute phase protein level'), ('MP:0000702', 'enlarged lymph nodes'), ('HP:0000509', None), ('MP:0011507', 'abnormal kidney thrombosis'), ('MP:0009507', 'abnormal mammary gland connective tissue morphology'), ('HP:0000502', None), ('MP:0006270', 'abnormal mammary gland growth during lactation'), ('HP:0011885', None), ('MP:0006137', 'venoocclusion'), ('HP:0002592', None), ('HP:0012647', None), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality'), ('MP:0001923', 'reduced female fertility'), ('HP:0002588', None), ('MP:0001139', 'abnormal vagina morphology'), ('HP:0001392', None), ('MP:0002249', 'abnormal larynx morphology'), ('MP:0010249', 'lactation failure'), ('MP:0001792', 'impaired wound healing'), ('MP:0005048', 'abnormal thrombosis'), ('MP:0009763', 'increased sensitivity to induced morbidity/mortality'), ('MP:0002282', 'abnormal trachea morphology'), ('MP:0001851', 'eye inflammation'), ('HP:0001824', None), ('MP:0005300', 'abnormal corneal stroma morphology'), ('MP:0000495', 'abnormal colon morphology'), ('MP:0008236', 'decreased susceptibility to neuronal excitotoxicity')]
MGI:3643679 Gm8682 []
MGI:95856 Gsta3 [('MP:0009766', 'increased sensitivity to xenobiotic induced morbidity/mortality'), ('MP:0008873', 'increased physiological sensitivity to xenobiotic')]
MGI:5455590 Gm25813 []
MGI:3651301 Gm14107 []
MGI:105103 Rprl3 []
MGI:5454298 Gm24521 []
MGI:1915951 Ppp1r27 []
MGI:106636 Atp5k []
MGI:3704327 Gm10182 []
MGI:3651595 Gm11295 []
MGI:3652326 Gm13902 []
MGI:3718464 Mir291b []
MGI:1918982 Vps11 []
MGI:5454076 Gm24299 []
MGI:1350917 Rps3 []
MGI:1913638 Cutc []
MGI:1914195 Sdha [('HP:0003228', None)]
MGI:1915254 Tmem9b []
MGI:3779470 Ces1b []
MGI:5454859 Gm25082 []
MGI:1926264 Tspan6 []
MGI:3651503 Gm13862 []
MGI:95862 Gstm4 [('MP:0001415', 'increased exploration in new environment'), ('HP:0001743', None)]
MGI:2444947 Mical2 []
MGI:5504138 Gm27023 []
MGI:5453406 Gm23629 []
MGI:3651534 Gm12258 []
MGI:3705806 Gm14536 []
MGI:1921611 4931429L15Rik []
MGI:3642824 Rpl9-ps7 []
MGI:1278340 Rpl21 []
MGI:4834232 Mir3060 []
MGI:107508 Ereg [('MP:0001194', 'dermatitis'), ('HP:0001824', None), ('MP:0008873', 'increased physiological sensitivity to xenobiotic')]
MGI:88343 Cd69 [('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('MP:0008719', 'impaired neutrophil recruitment'), ('HP:0003496', None), ('MP:0005387', 'immune system phenotype'), ('HP:0040238', None), ('MP:0008051', 'abnormal memory T cell physiology'), ('MP:0005463', 'abnormal CD4-positive, alpha-beta T cell physiology'), ('MP:0008561', 'decreased tumor necrosis factor secretion')]
MGI:3650581 Gm12419 []
MGI:3610314 Scimp [('HP:0001903', None), ('HP:0003124', None)]
MGI:1098779 Cdk2ap2 []
MGI:2144766 Slc25a47 []
MGI:2138935 Fam102a []
MGI:5434102 Ftl1-ps2 []
MGI:1925560 1810024B03Rik []
MGI:3704487 Amd-ps3 []
MGI:101921 Ap2a1 []
MGI:2685672 Gm826 []
MGI:3648378 Sult2a5 []
MGI:3648883 Rpl13a-ps1 []
MGI:1354945 Plpp2 [('MP:0002169', 'no abnormal phenotype detected')]
MGI:2146020 Mief1 []
MGI:1921808 Gvin1 []
MGI:2685015 Mtg1 []
MGI:106499 Ppih []
MGI:4421912 n-R5s67 []
MGI:3801856 Gm15956 []
MGI:1336892 Slc6a18 [('MP:0011417', 'abnormal renal transport'), ('HP:0000822', None)]
MGI:1914411 Sclt1 []
MGI:1915871 Mthfd2l []
MGI:1919405 Cenpn []
MGI:3612471 C330021F23Rik []
MGI:5568573 Rubie [('MP:0011238', 'abnormal inner ear development'), ('HP:0040106', None), ('HP:0000752', None)]
MGI:3648788 Gm5239 []
MGI:1352495 Zfp385a [('GO:0007611PHENOTYPE', None), ('GO:0007626PHENOTYPE', None), ('MP:0002417', 'abnormal megakaryocyte morphology'), ('MP:0001265', 'decreased body size'), ('MP:0002551', 'abnormal blood coagulation'), ('HP:0002170', None), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:1914535 Cwc27 [('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('HP:0003251', None), ('HP:0008222', None)]
MGI:3644274 Gm5687 []
MGI:99501 Fgb [('MP:0002059', 'abnormal seminal vesicle morphology')]
MGI:2140940 Acacb [('HP:0000708', None), ('HP:0002591', None), ('MP:0002188', 'small heart'), ('HP:0003292', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0005282', 'decreased fatty acid level'), ('HP:0000842', None), ('MP:0002575', 'increased circulating ketone body level'), ('HP:0012338', None), ('MP:0005439', 'decreased glycogen level'), ('HP:0011014', None), ('MP:0005289', 'increased oxygen consumption'), ('MP:0010379', 'decreased respiratory quotient'), ('MP:0002118', 'abnormal lipid homeostasis'), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:5455514 Gm25737 []
MGI:1922948 Fam35a []
MGI:2443301 Slc35c1 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0020332', 'impaired leukocyte tethering or rolling'), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0002345', 'abnormal lymph node primary follicle morphology'), ('HP:0004325', None), ('MP:0011084', 'lethality at weaning, incomplete penetrance'), ('MP:0001922', 'reduced male fertility'), ('MP:0003628', 'abnormal leukocyte adhesion'), ('MP:0000219', 'increased neutrophil cell number'), ('HP:0001939', None), ('HP:0012311', None), ('MP:0001183', 'overexpanded pulmonary alveoli'), ('MP:0000420', 'ruffled hair'), ('GO:0045746PHENOTYPE', None)]
MGI:1927086 Ube4b [('MP:0000278', 'abnormal myocardial fiber morphology'), ('HP:0001698', None), ('MP:0001787', 'pericardial edema'), ('GO:0044257PHENOTYPE', None), ('MP:0000876', 'Purkinje cell degeneration'), ('MP:0003225', 'axonal dystrophy'), ('HP:0002624', None), ('MP:0000877', 'abnormal Purkinje cell morphology'), ('MP:0001405', 'impaired coordination'), ('HP:0001640', None), ('GO:0006511PHENOTYPE', None), ('HP:0011017', None), ('MP:0003222', 'increased cardiomyocyte apoptosis'), ('HP:0030746', None)]
MGI:3819569 Snord98 []
MGI:3651577 Gm13666 []
MGI:98223 Saa3 []
MGI:1270860 Plscr2 [('HP:0005815', None)]
MGI:1921138 Ppp1r42 [('HP:0002948', None)]
MGI:107180 Elf1 []
MGI:3801877 Uckl1os []
MGI:3646659 Fbxw24 []
MGI:1923113 Clec4g [('HP:0012311', None), ('MP:0008078', 'increased CD8-positive, alpha-beta T cell number'), ('MP:0003131', 'increased erythrocyte cell number'), ('HP:0001873', None), ('HP:0100827', None), ('MP:0008074', 'increased CD4-positive, alpha beta T cell number')]
MGI:1916003 Mybphl [('MP:0004647', 'decreased lumbar vertebrae number'), ('MP:0010101', 'increased sacral vertebrae number')]
MGI:98625 Tcrg-C1 [('MP:0008757', 'abnormal T cell receptor gamma chain V-J recombination'), ('GO:0048873PHENOTYPE', None)]
MGI:1918397 Oxsm []
MGI:1923890 1700113H08Rik []
MGI:1860137 Gp9 [('HP:0001873', None)]
MGI:99207 Zfp60 []
MGI:5453219 Gm23442 []
MGI:2142174 4933405O20Rik []
MGI:3809043 Rps8-ps4 []
MGI:1914361 Naaa [('HP:0040216', None)]
MGI:3643262 Gm9392 []
MGI:1922264 4930503B20Rik []
MGI:101899 Pla2g5 [('MP:0000343', 'altered response to myocardial infarction'), ('MP:0003038', 'decreased myocardial infarction size'), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic')]
MGI:3650064 Gm11517 []
MGI:3705509 Gm14633 []
MGI:1933161 Trim23 []
MGI:3643578 Gm6793 []
MGI:1923301 Ganc []
MGI:3651124 Gm12248 []
MGI:108414 Pafah1b3 [('MP:0005380', 'embryo phenotype'), ('GO:0007283PHENOTYPE', None)]
MGI:1194499 Gsg1 []
MGI:3649243 Gm13231 []
MGI:1914616 Tmed11 []
MGI:1918436 4933417D19Rik []
MGI:3642408 Gm10250 []
MGI:1926022 Hhipl2 []
MGI:1196608 Slc4a1ap []
MGI:1346051 Dut []
MGI:1098533 Ints9 []
MGI:3646055 Gm5801 []
MGI:2140201 Slc5a9 []
MGI:5456270 Gm26493 []
MGI:5455464 Gm25687 []
MGI:2444813 9030617O03Rik []
MGI:3579898 Rhox8 []
MGI:3704480 Gm10184 []
MGI:5452188 Gm22411 []
MGI:1914830 Lrrc18 []
MGI:1201780 Atp6v1a []
MGI:3642024 Gm10722 []
MGI:2140910 AI480526 []
MGI:1920603 Actrt2 []
MGI:4421954 n-R5s106 []
MGI:5455261 Gm25484 []
MGI:1349763 Dpysl2 [('MP:0008143', 'abnormal dendrite morphology'), ('MP:0003631', 'nervous system phenotype')]
MGI:1914391 Fbxo5 [('MP:0002663', 'absent blastocoele'), ('MP:0005031', 'abnormal trophoblast layer morphology'), ('MP:0001672', 'abnormal embryo development'), ('MP:0002718', 'abnormal inner cell mass morphology'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance')]
MGI:1196368 Carhsp1 []
MGI:5453712 Gm23935 []
MGI:1924058 Rpl18a []
MGI:1923539 Phf14 [('HP:0002093', None), ('MP:0000351', 'increased cell proliferation'), ('MP:0008277', 'abnormal sternum ossification'), ('MP:0010903', 'abnormal pulmonary alveolus wall morphology')]
MGI:88518 Cryba1 [('HP:0100018', None), ('MP:0002840', 'abnormal lens fiber morphology'), ('HP:0008063', None), ('MP:0020378', 'abnormal cell cytoskeleton morphology'), ('MP:0012143', 'decreased a wave amplitude'), ('MP:0008260', 'abnormal autophagy'), ('HP:0012379', None), ('HP:0000568', None), ('MP:0012144', 'decreased b wave amplitude'), ('MP:0005201', 'abnormal retinal pigment epithelium morphology'), ('MP:0005058', 'abnormal lysosome morphology'), ('GO:0043010PHENOTYPE', None), ('HP:0000546', None), ('HP:0002171', None), ('HP:0010700', None), ('MP:0003172', 'abnormal lysosome physiology')]
MGI:3644473 Gm7335 []
MGI:1921406 Acot12 []
MGI:99927 mt-Atp6 []
MGI:3651700 Gm11752 []
MGI:1922717 Stpg4 []
MGI:3646150 Gm7964 []
MGI:2146565 Nsun3 []
MGI:104645 Hsd3b5 []
MGI:103164 Mif-ps3 []
MGI:109568 Wbp4 []
MGI:3704271 Gm9769 []
MGI:3641929 Rps19-ps9 []
MGI:4937884 Gm17057 []
MGI:108247 Tdg [('MP:0004181', 'abnormal carotid artery morphology'), ('MP:0006126', 'abnormal cardiac outflow tract development'), ('MP:0008877', 'abnormal DNA methylation'), ('MP:0003984', 'embryonic growth retardation'), ('MP:0001787', 'pericardial edema'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0003888', 'liver hemorrhage'), ('HP:0011851', None), ('MP:0009797', 'abnormal mismatch repair'), ('HP:0011029', None), ('MP:0006279', 'abnormal limb development'), ('MP:0003920', 'abnormal heart right ventricle morphology'), ('HP:0001892', None)]
MGI:107741 Pvr [('MP:0005025', 'abnormal response to infection'), ('HP:0002720', None), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic')]
MGI:1923274 Gm11346 []
MGI:2387217 Ift52 [('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance')]
MGI:4937289 Gm17655 []
MGI:5455766 Gm25989 []
MGI:3644711 Gm8163 []
MGI:1919335 Osgepl1 []
MGI:5454593 Gm24816 []
MGI:1929259 Asic5 []
MGI:3643448 Gm7931 []
MGI:3650377 Gm12275 []
MGI:3648582 Gm4895 []
MGI:3652029 Gm11877 []
MGI:5456022 Gm26245 []
MGI:3650191 Gm14480 []
MGI:3649722 Gm13007 []
MGI:3642342 Gm10086 []
MGI:3650060 Gm14269 []
MGI:97604 Pklr [('MP:0002875', 'decreased erythrocyte cell number'), ('MP:0000245', 'abnormal erythropoiesis'), ('HP:0001877', None), ('MP:0011178', 'increased erythroblast number'), ('MP:0010035', 'increased erythrocyte clearance'), ('GO:0051707PHENOTYPE', None), ('MP:0002874', 'decreased hemoglobin content'), ('HP:0001878', None), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0000208', 'decreased hematocrit'), ('HP:0012132', None), ('HP:0001744', None), ('MP:0010751', 'decreased susceptibility to parasitic infection induced morbidity/mortality')]
MGI:97810 Ptprc [('MP:0008351', 'decreased gamma-delta intraepithelial T cell number'), ('MP:0000693', 'spleen hyperplasia'), ('MP:0008168', 'decreased B-1a cell number'), ('MP:0003944', 'abnormal T cell subpopulation ratio'), ('MP:0002418', 'increased susceptibility to viral infection'), ('MP:0000702', 'enlarged lymph nodes'), ('MP:0002398', 'abnormal bone marrow cell morphology/development'), ('HP:0002014', None), ('MP:0005092', 'decreased double-positive T cell number'), ('HP:0002090', None), ('MP:0005597', 'decreased susceptibility to type I hypersensitivity reaction'), ('HP:0012177', None), ('MP:0010136', 'decreased DN4 thymocyte number'), ('MP:0010839', 'decreased CD8-positive, alpha-beta memory T cell number'), ('MP:0008894', 'abnormal intraepithelial T cell morphology'), ('HP:0010987', None), ('MP:0010133', 'increased DN3 thymocyte number'), ('MP:0008080', 'abnormal CD8-positive, alpha-beta T cell differentiation'), ('GO:0045577PHENOTYPE', None), ('HP:0040006', None), ('MP:0008044', 'increased NK cell number'), ('HP:0001744', None), ('HP:0010976', None), ('HP:0010975', None), ('MP:0009808', 'decreased oligodendrocyte number'), ('MP:0001541', 'abnormal osteoclast physiology'), ('MP:0000245', 'abnormal erythropoiesis'), ('HP:0001882', None), ('MP:0001800', 'abnormal humoral immune response'), ('MP:0000715', 'decreased thymocyte number'), ('HP:0000083', None), ('MP:0005350', 'increased susceptibility to autoimmune disorder'), ('MP:0005095', 'decreased T cell proliferation'), ('MP:0008895', 'abnormal intraepithelial T cell number'), ('HP:0011840', None), ('MP:0008048', 'abnormal memory T cell number'), ('HP:0012115', None), ('MP:0008076', 'abnormal CD4-positive T cell differentiation'), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0010836', 'decreased CD4-positive, alpha-beta memory T cell number'), ('MP:0011084', 'lethality at weaning, incomplete penetrance'), ('GO:0030183PHENOTYPE', None), ('MP:0001828', 'abnormal T cell activation'), ('MP:0008211', 'decreased mature B cell number'), ('HP:0002846', None), ('MP:0008207', 'decreased B-2 B cell number'), ('MP:0004977', 'increased B-1 B cell number'), ('HP:0011893', None), ('HP:0002716', None), ('HP:0005404', None), ('HP:0001978', None), ('HP:0011115', None)]
MGI:3780193 Mup12 []
MGI:2385955 Defb19 []
MGI:3650009 Gm14052 []
MGI:1096353 Khk [('MP:0005380', 'embryo phenotype')]
MGI:5521097 Gm27254 []
MGI:1201607 Blzf1 []
MGI:5452551 Gm22774 []
MGI:5477346 Gm26852 []
MGI:5453718 Gm23941 []
MGI:3642193 Rpl19-ps11 []
MGI:1919104 Dpep3 []
MGI:1201387 Nlk [('MP:0011091', 'prenatal lethality, complete penetrance'), ('MP:0002217', 'small lymph nodes'), ('MP:0008208', 'decreased pro-B cell number'), ('MP:0000715', 'decreased thymocyte number'), ('MP:0001602', 'impaired myelopoiesis'), ('MP:0000333', 'decreased bone marrow cell number'), ('MP:0001405', 'impaired coordination'), ('HP:0040218', None), ('MP:0002722', 'abnormal immune system organ morphology')]
MGI:2387642 Cxcl17 [('HP:0001627', None), ('HP:0009887', None)]
MGI:107161 Cst8 []
MGI:5456092 Gm26315 []
MGI:96945 Smcp []
MGI:5454482 Gm24705 []
MGI:5453509 Gm23732 []
MGI:3819551 Snord69 []
MGI:3801917 Hopxos []
MGI:2138953 Fibcd1 []
MGI:3651545 Rpsa-ps4 []
MGI:3030536 Olfr702 []
MGI:5455227 Gm25450 []
MGI:1933156 Acox3 []
MGI:5455431 Gm25654 []
MGI:3708729 Gm9825 []
MGI:1915112 2310033P09Rik []
MGI:3651345 Gm11450 []
MGI:3645317 Gm16412 []
MGI:1913954 Rbm4b [('HP:0003074', None), ('GO:0035883PHENOTYPE', None), ('HP:0000833', None)]
MGI:5453045 Gm23268 []
MGI:1099439 Stk10 []
MGI:2685142 Olfm4 [('HP:0002718', None), ('MP:0010377', 'abnormal gut flora balance'), ('MP:0008713', 'abnormal cytokine level')]
MGI:1913756 Psmg3 []
MGI:3041188 Fam69c []
MGI:1925953 Zfp972 []
MGI:1859822 Crtam [('MP:0008682', 'decreased interleukin-17 secretion'), ('MP:0005387', 'immune system phenotype'), ('MP:0008078', 'increased CD8-positive, alpha-beta T cell number'), ('MP:0005463', 'abnormal CD4-positive, alpha-beta T cell physiology'), ('HP:0002718', None), ('HP:0011117', None), ('MP:0008074', 'increased CD4-positive, alpha beta T cell number')]
MGI:5452880 Gm23103 []
MGI:1917951 Nipal1 []
MGI:1338820 Bmp10 [('HP:0000003', None), ('HP:0012101', None), ('HP:0004971', None), ('MP:0010725', 'thin interventricular septum'), ('MP:0000291', 'enlarged pericardium'), ('HP:0000967', None), ('MP:0001730', 'embryonic growth arrest'), ('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0004076', 'abnormal vitelline vascular remodeling'), ('HP:0000778', None)]
MGI:5452925 Gm23148 []
MGI:97473 Pah [('HP:0001508', None), ('MP:0005332', 'abnormal amino acid level'), ('MP:0001265', 'decreased body size'), ('MP:0001496', 'audiogenic seizures'), ('MP:0001525', 'impaired balance'), ('HP:0003112', None), ('HP:0000568', None), ('MP:0009358', 'environmentally induced seizures'), ('GO:0004505PHENOTYPE', None)]
MGI:3647751 Gm4987 []
MGI:1917895 Oip5 []
MGI:4421901 n-R5s56 []
MGI:1888978 Ntn4 [('HP:0010783', None), ('MP:0002792', 'abnormal retinal vasculature morphology'), ('HP:0000525', None), ('MP:0010144', 'abnormal tumor vascularization'), ('MP:0000351', 'increased cell proliferation'), ('MP:0001265', 'decreased body size'), ('HP:0002597', None), ('MP:0009450', 'abnormal axon fasciculation')]
MGI:2152844 Slc2a9 [('HP:0000123', None), ('HP:0000803', None), ('HP:0001395', None), ('HP:0004325', None), ('HP:0003259', None), ('HP:0001959', None), ('MP:0011925', 'abnormal heart echocardiography feature'), ('MP:0009350', 'decreased urine pH'), ('HP:0003081', None), ('MP:0008965', 'increased basal metabolism'), ('HP:0000126', None), ('HP:0001397', None), ('MP:0010359', 'increased liver free fatty acids level'), ('MP:0005289', 'increased oxygen consumption'), ('HP:0002155', None), ('HP:0000855', None), ('MP:0010107', 'abnormal renal reabsorbtion'), ('MP:0005367', 'renal/urinary system phenotype'), ('MP:0008882', 'abnormal enterocyte physiology'), ('HP:0003149', None), ('HP:0000092', None), ('HP:0001939', None), ('MP:0010379', 'decreased respiratory quotient'), ('MP:0003868', 'abnormal feces composition'), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0002048', None), ('HP:0000787', None), ('HP:0012611', None), ('HP:0003124', None)]
MGI:88599 Cyp2b13 [('MP:0010851', 'decreased effector memory CD8-positive, alpha-beta T cell number')]
MGI:99613 Zap70 [('MP:0002432', 'abnormal CD4-positive, alpha beta T cell morphology'), ('MP:0004919', 'abnormal positive T cell selection'), ('MP:0002217', 'small lymph nodes'), ('GO:0046777PHENOTYPE', None), ('MP:0001829', 'increased activated T cell number'), ('GO:0035556PHENOTYPE', None), ('MP:0003944', 'abnormal T cell subpopulation ratio'), ('MP:0008826', 'abnormal splenic cell ratio'), ('HP:0002090', None), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('MP:0002408', 'abnormal double-positive T cell morphology'), ('MP:0003790', 'absent CD4-positive, alpha beta T cells'), ('MP:0008080', 'abnormal CD8-positive, alpha-beta T cell differentiation'), ('HP:0001370', None), ('MP:0004974', 'decreased regulatory T cell number'), ('MP:0008049', 'increased memory T cell number'), ('MP:0002145', 'abnormal T cell differentiation'), ('MP:0001825', 'arrested T cell differentiation'), ('MP:0008070', 'absent T cells'), ('HP:0005415', None), ('HP:0011840', None), ('MP:0004816', 'abnormal class switch recombination'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0008828', 'abnormal lymph node cell ratio'), ('HP:0000939', None), ('MP:0008499', 'increased IgG1 level'), ('MP:0008827', 'abnormal thymus cell ratio'), ('GO:0004713PHENOTYPE', None), ('HP:0005403', None), ('MP:0001828', 'abnormal T cell activation'), ('MP:0004972', 'abnormal regulatory T cell number'), ('MP:0003725', 'increased autoantibody level'), ('HP:0001367', None), ('HP:0000778', None), ('MP:0001606', 'impaired hematopoiesis')]
MGI:1339962 Ftcd []
MGI:88603 Cyp2d11 []
ENSEMBL:ENSMUSG00000099077 Ppp2r3d []
MGI:5456072 Gm26295 []
MGI:3648632 Gm8814 []
MGI:3648561 Gm9169 []
MGI:1929217 Ap3s1-ps2 []
MGI:2385211 Fam76a []
MGI:2178742 Stab1 []
MGI:1277143 Gtf2h3 []
MGI:1920185 Ddx41 [('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0013293', 'embryonic lethality prior to tooth bud stage')]
MGI:1920648 Nipsnap3a []
MGI:2442629 Mfsd7a []
MGI:97429 Oas1g [('MP:0004651', 'increased thoracic vertebrae number'), ('HP:0005815', None)]
MGI:97178 Map4 []
MGI:3645529 Cyp21a2-ps []
MGI:2679274 Adck5 []
MGI:97828 Pygb []
MGI:5452140 Gm22363 []
MGI:2385046 Slc26a8 [('MP:0009243', 'hairpin sperm flagellum'), ('MP:0009831', 'abnormal sperm midpiece morphology'), ('MP:0009832', 'abnormal sperm mitochondrial sheath morphology'), ('MP:0004542', 'impaired acrosome reaction'), ('MP:0002675', 'asthenozoospermia')]
MGI:3644440 Gm6175 []
MGI:5530773 Mir6353 []
MGI:2442858 Ddx58 [('MP:0011085', 'postnatal lethality, complete penetrance'), ('MP:0005387', 'immune system phenotype'), ('HP:0005403', None), ('MP:0011108', 'embryonic lethality during organogenesis, incomplete penetrance'), ('MP:0001829', 'increased activated T cell number'), ('HP:0001978', None), ('MP:0002462', 'abnormal granulocyte physiology'), ('HP:0002846', None), ('MP:0002418', 'increased susceptibility to viral infection'), ('HP:0005237', None), ('HP:0001911', None), ('MP:0008750', 'abnormal interferon level'), ('HP:0001744', None), ('MP:0008049', 'increased memory T cell number')]
MGI:3643116 Gm8185 []
MGI:894689 Ywhae [('MP:0004759', 'decreased mitotic index'), ('MP:0008022', 'dilated heart ventricle'), ('MP:0011390', 'abnormal fetal cardiomyocyte physiology'), ('MP:0000788', 'abnormal cerebral cortex morphology'), ('GO:0001764PHENOTYPE', None), ('MP:0008284', 'abnormal hippocampus pyramidal cell layer'), ('HP:0002269', None), ('HP:0000961', None), ('GO:0021766PHENOTYPE', None)]
MGI:3652220 Hnf1aos2 []
MGI:102504 mt-Co1 [('HP:0001508', None), ('MP:0000278', 'abnormal myocardial fiber morphology'), ('HP:0004325', None), ('MP:0001853', 'heart inflammation'), ('MP:0008775', 'abnormal heart ventricle pressure'), ('MP:0004215', 'abnormal myocardial fiber physiology'), ('HP:0002597', None), ('MP:0013405', 'increased circulating lactate level'), ('HP:0003198', None)]
MGI:3780550 Mthfsl [('MP:0010053', 'decreased grip strength')]
MGI:104768 Gast [('MP:0003892', 'abnormal gastric gland morphology'), ('MP:0004499', 'increased incidence of tumors by chemical induction'), ('HP:0000842', None), ('MP:0004140', 'abnormal gastric chief cell morphology'), ('HP:0011031', None), ('MP:0000495', 'abnormal colon morphology'), ('MP:0000501', 'abnormal digestive secretion'), ('MP:0003564', 'abnormal insulin secretion')]
MGI:3652081 Rpsa-ps3 []
MGI:3704287 Gm10175 []
MGI:3783229 Gm15787 []
MGI:3644566 Gm8731 []
MGI:3650777 Gm14049 []
MGI:3651698 Gm14239 []
MGI:109565 Kmt2b [('MP:0003787', 'abnormal imprinting'), ('HP:0000969', None), ('MP:0008871', 'abnormal ovarian follicle number'), ('MP:0003059', 'decreased insulin secretion'), ('HP:0011969', None), ('MP:0009288', 'increased epididymal fat pad weight'), ('MP:0009648', 'abnormal superovulation'), ('HP:0001397', None), ('MP:0005181', 'decreased circulating estradiol level'), ('MP:0009289', 'decreased epididymal fat pad weight'), ('MP:0010359', 'increased liver free fatty acids level'), ('HP:0000855', None), ('MP:0012157', 'rostral body truncation'), ('MP:0011092', 'embryonic lethality, complete penetrance'), ('MP:0001923', 'reduced female fertility'), ('MP:0000929', 'open neural tube'), ('GO:0007613PHENOTYPE', None), ('HP:0000234', None), ('MP:0000269', 'abnormal heart looping'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0003984', 'embryonic growth retardation'), ('HP:0000842', None), ('MP:0006042', 'increased apoptosis'), ('MP:0001688', 'abnormal somite development')]
MGI:3037698 Gm1840 []
MGI:3782787 Gm4604 []
MGI:1921443 Ccdc9 []
MGI:3779200 Gm10985 []
MGI:103238 Cyp2c29 []
MGI:5504161 Gm27046 []
MGI:5455658 Gm25881 []
MGI:1918017 Prpf3 [('MP:0005391', 'vision/eye phenotype'), ('MP:0011092', 'embryonic lethality, complete penetrance'), ('HP:0000546', None)]
MGI:2152878 A1bg []
MGI:4421983 n-R5s127 []
MGI:4834247 Mir3074-1 []
MGI:3643593 Gm8210 [('HP:0001508', None), ('MP:0011704', 'decreased fibroblast proliferation'), ('HP:0000823', None), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('GO:0035264PHENOTYPE', None), ('MP:0000060', 'delayed bone ossification'), ('GO:0006412PHENOTYPE', None), ('GO:0008283PHENOTYPE', None), ('HP:0011018', None)]
MGI:3612701 Spdye4b []
MGI:1914006 Cfap97 []
MGI:3644851 Gm7336 []
MGI:1914722 Mtfr1 [('MP:0010956', 'abnormal mitochondrial ATP synthesis coupled electron transport')]
MGI:1915317 Mrnip [('HP:0000752', None)]
MGI:106651 Ly6e [('GO:0030325PHENOTYPE', None), ('GO:0055010PHENOTYPE', None), ('MP:0001740', 'failure of adrenal epinephrine secretion'), ('MP:0005663', 'abnormal circulating noradrenaline level'), ('MP:0008288', 'abnormal adrenal cortex morphology'), ('GO:0001701PHENOTYPE', None), ('GO:0035265PHENOTYPE', None)]
MGI:3705695 Gm14516 []
MGI:3650190 Gm14286 []
MGI:3649910 Gm11619 []
MGI:3651800 Gm14373 []
MGI:2387201 Yrdc []
MGI:4415005 Gm16585 []
MGI:5452548 Gm22771 []
MGI:1918464 Ppm1f [('HP:0000752', None)]
MGI:1920905 Pex11g []
MGI:95403 Stom []
MGI:5453837 Gm24060 []
MGI:2148248 1700102P08Rik []
MGI:109266 Gzma [('MP:0005079', 'decreased cytotoxic T cell cytolysis'), ('MP:0005387', 'immune system phenotype'), ('MP:0003721', 'increased tumor growth/size')]
MGI:1919519 Cda []
MGI:2140998 Ube3c [('HP:0010679', None), ('MP:0005387', 'immune system phenotype'), ('HP:0005736', None), ('MP:0001258', 'decreased body length'), ('MP:0013513', 'decreased memory-marker CD4-negative NK T cell number'), ('MP:0003961', 'decreased lean body mass'), ('MP:0013522', 'decreased memory-marker CD4-positive NK T cell number'), ('MP:0013678', 'decreased Ly6C-positive NK T cell number'), ('MP:0011940', 'decreased food intake')]
MGI:3649154 Rps12-ps22 []
MGI:4422025 n-R5s161 []
MGI:3708759 Gm10602 []
MGI:3629652 Mir697 []
MGI:1925106 Ankrd66 []
MGI:3834078 Gm15832 []
MGI:2443977 Gchfr []
MGI:3651407 Gm13363 []
MGI:88540 Csn1s1 [('MP:0004047', 'abnormal milk composition')]
MGI:2385053 Rrp36 []
MGI:3649935 Gm13320 []
MGI:2444029 Ankrd52 []
MGI:1913362 Marc1 []
MGI:1922942 Nr2c2ap []
MGI:2180203 Tmlhe []
MGI:102475 mt-Ts1 []
MGI:1931463 Sult5a1 []
MGI:2142208 Faap24 []
MGI:3651653 Gm11285 []
MGI:4421907 n-R5s62 []
MGI:3651744 Gm13141 []
MGI:5452530 Gm22753 []
MGI:2384812 Lpcat1 [('MP:0008450', 'retinal photoreceptor degeneration'), ('MP:0005551', 'abnormal eye electrophysiology'), ('HP:0000504', None), ('MP:0008444', 'retinal cone cell degeneration'), ('MP:0012029', 'abnormal electroretinogram waveform feature'), ('MP:0008451', 'retinal rod cell degeneration'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('MP:0001182', 'lung hemorrhage'), ('HP:0000961', None), ('MP:0011965', 'decreased total retina thickness'), ('HP:0000546', None)]
MGI:1353627 Angptl3 [('MP:0002981', 'increased liver weight'), ('HP:0012153', None), ('MP:0005318', 'decreased triglyceride level'), ('MP:0009289', 'decreased epididymal fat pad weight'), ('MP:0000183', 'decreased circulating LDL cholesterol level'), ('MP:0005146', 'decreased circulating VLDL cholesterol level'), ('MP:0005375', 'adipose tissue phenotype'), ('HP:0003233', None)]
MGI:5531399 Gm28017 []
MGI:102563 Dct [('GO:0043473PHENOTYPE', None), ('GO:0048468PHENOTYPE', None), ('HP:0008034', None), ('MP:0005391', 'vision/eye phenotype'), ('HP:0009887', None)]
MGI:1914652 Txndc8 []
MGI:3646907 Gm6563 []
MGI:3643270 Gm9386 []
MGI:3652270 Gm13012 []
MGI:96676 Kin []
MGI:5530897 Mir7060 []
MGI:3649301 Gm13577 []
MGI:109254 Gzmf []
MGI:1915271 Bphl [('MP:0011967', 'increased or absent threshold for auditory brainstem response'), ('HP:0012101', None)]
MGI:5521072 Zfp-ps []
MGI:5530891 Gm27509 []
MGI:2181510 Dhrsx []
MGI:2449939 Zgpat [('MP:0013279', 'increased fasted circulating glucose level')]
MGI:99500 Ephx2 [('MP:0002792', 'abnormal retinal vasculature morphology'), ('HP:0001939', None)]
MGI:5453401 Gm23624 []
MGI:3782245 Gm4070 []
MGI:3643790 Gm5451 []
MGI:2387153 Npb []
MGI:3652235 Rps19-ps7 []
MGI:1913449 Commd4 []
MGI:3782973 Gm15526 []
MGI:3650507 Gm11868 []
MGI:1202298 Nmt2 []
MGI:3782325 Gm4149 []
MGI:1914282 Snapc5 []
MGI:97766 Prm2 [('HP:0000144', None), ('GO:0007286PHENOTYPE', None), ('HP:0000789', None), ('HP:0012206', None)]
MGI:3651418 Gm11893 []
MGI:1341284 Scnm1 [('GO:0008380PHENOTYPE', None)]
MGI:2444491 Heatr3 []
MGI:1347055 Vnn3 []
MGI:1921587 Palm3 []
MGI:5452105 Gm22328 []
MGI:2445042 A230107N01Rik []
MGI:1914246 Ceacam11 []
MGI:3649742 Gm12226 []
MGI:1891917 Ywhab []
MGI:107283 Rab6b [('MP:0005387', 'immune system phenotype')]
MGI:1315204 Slc40a1 [('HP:0001903', None), ('MP:0005562', 'decreased mean corpuscular hemoglobin'), ('MP:0013293', 'embryonic lethality prior to tooth bud stage'), ('MP:0009322', 'increased splenocyte apoptosis'), ('HP:0000938', None), ('MP:0004151', 'decreased circulating iron level'), ('HP:0000568', None), ('MP:0003998', 'decreased thermal nociceptive threshold'), ('MP:0011106', 'embryonic lethality between implantation and somite formation, incomplete penetrance'), ('MP:0005638', 'hemochromatosis'), ('MP:0005564', 'increased hemoglobin content'), ('HP:0001923', None), ('HP:0012465', None), ('MP:0005391', 'vision/eye phenotype'), ('MP:0011890', 'increased circulating ferritin level'), ('HP:0004840', None), ('MP:0009323', 'abnormal spleen development'), ('MP:0003131', 'increased erythrocyte cell number'), ('MP:0011897', 'decreased circulating unsaturated transferrin level'), ('MP:0020367', 'increased heart iron level'), ('HP:0040006', None), ('HP:0010504', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0008737', 'abnormal spleen physiology'), ('MP:0010124', 'decreased bone mineral content'), ('HP:0001877', None), ('HP:0001392', None), ('MP:0013302', 'increased pancreas iron level'), ('HP:0012675', None), ('HP:0011031', None), ('HP:0011273', None), ('MP:0008809', 'increased spleen iron level'), ('HP:0002910', None), ('MP:0001698', 'decreased embryo size'), ('MP:0011097', 'embryonic lethality between somite formation and embryo turning, complete penetrance'), ('HP:0005518', None), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0003631', 'nervous system phenotype'), ('MP:0002591', 'decreased mean corpuscular volume'), ('GO:0055072PHENOTYPE', None), ('MP:0001265', 'decreased body size'), ('MP:0010375', 'increased kidney iron level'), ('MP:0000692', 'small spleen')]
MGI:1913315 Hsd17b14 []
MGI:1921272 Glyr1 []
MGI:3032634 Ugt1a5 []
MGI:3783155 Gm15713 []
MGI:1922375 4930513O06Rik []
MGI:1914386 Kbtbd4 []
MGI:97518 Cdk18 []
MGI:3646708 Gm16409 []
MGI:1934682 Emc9 []
MGI:3652253 Gm14414 []
MGI:3702407 Gm14416 []
MGI:96018 Hba-ps4 []
MGI:2442653 Rbm48 []
MGI:2682318 Zfp395 []
MGI:2148523 Vmn1r29 []
MGI:1917473 Nars []
MGI:88095 Serpinc1 [('MP:0002083', 'premature death'), ('HP:0200023', None), ('MP:0001935', 'decreased litter size'), ('HP:0001397', None), ('HP:0012372', None), ('HP:0001627', None), ('MP:0011099', 'lethality throughout fetal growth and development, complete penetrance'), ('MP:0002658', 'abnormal liver regeneration'), ('MP:0002698', 'abnormal sclera morphology'), ('HP:0001744', None), ('MP:0002847', 'abnormal renal glomerular filtration rate'), ('MP:0005098', 'abnormal optic choroid morphology'), ('MP:0020407', 'abnormal placental thrombosis'), ('MP:0006137', 'venoocclusion'), ('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0002551', 'abnormal blood coagulation'), ('MP:0005329', 'abnormal myocardium layer morphology'), ('HP:0012115', None)]
MGI:2673990 Vaultrc5 []
MGI:1915737 Tmem140 []
MGI:1925680 Prr9 []
MGI:1310000 Kcnj15 []
MGI:3649994 Gm13586 []
MGI:97370 Enpp1 [('MP:0004181', 'abnormal carotid artery morphology'), ('MP:0002083', 'premature death'), ('HP:0004325', None), ('MP:0006357', 'abnormal circulating mineral level'), ('HP:0000938', None), ('MP:0004231', 'abnormal calcium ion homeostasis'), ('HP:0002754', None), ('MP:0003653', 'decreased skin turgor'), ('HP:0100774', None), ('HP:0007862', None), ('HP:0005645', None), ('MP:0002066', 'abnormal motor capabilities/coordination/movement'), ('HP:0011729', None), ('MP:0001505', 'hunched posture'), ('MP:0020039', 'increased bone ossification'), ('HP:0007973', None), ('MP:0001565', 'abnormal circulating phosphate level'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0010124', 'decreased bone mineral content'), ('HP:0100671', None), ('MP:0011584', 'increased alkaline phosphatase activity'), ('MP:0010234', 'abnormal vibrissa follicle morphology'), ('MP:0000166', 'abnormal chondrocyte morphology'), ('MP:0009820', 'abnormal liver vasculature morphology'), ('HP:0002143', None), ('MP:0009763', 'increased sensitivity to induced morbidity/mortality'), ('HP:0001508', None), ('MP:0001533', 'abnormal skeleton physiology'), ('MP:0003196', 'calcified skin'), ('HP:0002650', None), ('MP:0005592', 'abnormal vascular smooth muscle morphology'), ('MP:0004173', 'abnormal intervertebral disk morphology'), ('HP:0004963', None), ('HP:0100769', None), ('HP:0000365', None), ('MP:0013941', 'abnormal enthesis morphology'), ('HP:0001367', None), ('HP:0002758', None), ('HP:0011138', None)]
MGI:3779896 Ccnb2-ps []
MGI:1927099 Rplp1 []
MGI:1915930 Fitm1 []
MGI:2181366 Capn8 []
MGI:3643411 Gm5422 []
MGI:3648259 Gm6768 []
MGI:5455354 Gm25577 []
MGI:1918049 Cep192 []
MGI:5455351 Gm25574 []
MGI:1918249 Naa40 [('MP:0005559', 'increased circulating glucose level'), ('HP:0004324', None), ('MP:0014171', 'increased fatty acid oxidation'), ('MP:0000183', 'decreased circulating LDL cholesterol level'), ('MP:0010360', 'decreased liver free fatty acids level'), ('MP:0002310', 'decreased susceptibility to hepatic steatosis'), ('MP:0004889', 'increased energy expenditure'), ('HP:0100738', None), ('MP:0010379', 'decreased respiratory quotient'), ('MP:0003976', 'decreased circulating VLDL triglyceride level'), ('MP:0005292', 'improved glucose tolerance')]
MGI:5453825 Gm24048 []
MGI:99182 Zscan21 [('MP:0009004', 'progressive hair loss'), ('HP:0001595', None), ('MP:0008925', 'increased cerebellar granule cell number'), ('MP:0003941', 'abnormal skin development'), ('MP:0000886', 'abnormal cerebellar granule layer morphology'), ('HP:0001006', None), ('MP:0002015', 'epithelioid cysts'), ('MP:0000855', 'accelerated formation of intralobular fissures')]
MGI:97849 Rag2 [('HP:0001888', None), ('HP:0002720', None), ('MP:0009540', "absent Hassall's corpuscle"), ('MP:0008479', 'decreased spleen white pulp amount'), ('MP:0005092', 'decreased double-positive T cell number'), ('HP:0002664', None), ('HP:0002732', None), ('MP:0008213', 'absent immature B cells'), ('HP:0002090', None), ('MP:0002401', 'abnormal lymphopoiesis'), ('HP:0002719', None), ('MP:0000511', 'abnormal intestinal mucosa morphology'), ('MP:0008182', 'decreased marginal zone B cell number'), ('MP:0000333', 'decreased bone marrow cell number'), ('MP:0010133', 'increased DN3 thymocyte number'), ('HP:0004315', None), ('MP:0002722', 'abnormal immune system organ morphology'), ('HP:0010976', None), ('MP:0005671', 'abnormal response to transplant'), ('MP:0008356', 'abnormal gamma-delta T cell differentiation'), ('HP:0001019', None), ('MP:0005095', 'decreased T cell proliferation'), ('MP:0008174', 'decreased follicular B cell number'), ('MP:0002407', 'abnormal double-negative T cell morphology'), ('MP:0008097', 'increased plasma cell number'), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0004326', None), ('MP:0002403', 'abnormal pre-B cell morphology'), ('MP:0008209', 'decreased pre-B cell number'), ('HP:0004295', None), ('MP:0002435', 'abnormal effector T cell morphology'), ('HP:0002037', None), ('HP:0002846', None), ('HP:0002718', None), ('MP:0002831', "absent Peyer's patches"), ('MP:0001802', 'arrested B cell differentiation'), ('MP:0001265', 'decreased body size'), ('MP:0000696', "abnormal Peyer's patch morphology")]
MGI:105305 Slc1a5 []
MGI:1914846 Tespa1 [('MP:0013588', 'small thymus medulla'), ('MP:0008567', 'decreased interferon-gamma secretion'), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('MP:0008049', 'increased memory T cell number'), ('HP:0005403', None)]
MGI:1096868 Cxcl5 [('MP:0000322', 'increased granulocyte number'), ('MP:0008597', 'decreased circulating interleukin-6 level'), ('MP:0010209', 'abnormal circulating chemokine level'), ('HP:0001875', None), ('MP:0008734', 'decreased susceptibility to endotoxin shock'), ('MP:0008706', 'decreased interleukin-6 secretion'), ('MP:0008563', 'decreased interferon-alpha secretion')]
MGI:97072 Gbp4 []
MGI:3646665 Gm5457 []
MGI:2384581 Ces4a []
MGI:3648043 Rybp-ps []
MGI:3645843 Gm7765 []
MGI:3028580 Cyp4a31 []
MGI:2446144 March9 [('HP:0003330', None), ('HP:0004325', None)]
MGI:2445210 Cyp4f39 []
MGI:3612288 Cyp2c67 []
MGI:3651005 Gm13436 []
MGI:5455059 Gm25282 []
MGI:2674130 Cnksr3 []
MGI:3711310 9930111J21Rik2 []
MGI:5452978 Gm23201 []
MGI:5454374 Gm24597 []
MGI:96211 Hp [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0000332', 'hemoglobinemia'), ('HP:0012211', None), ('MP:0002703', 'abnormal renal tubule morphology'), ('MP:0003917', 'increased kidney weight'), ('MP:0005325', 'abnormal renal glomerulus morphology'), ('MP:0004756', 'abnormal proximal convoluted tubule morphology')]
MGI:1929278 Tmem59 []
MGI:101788 Cisd3 []
MGI:96247 Hsp90ab1 [('MP:0001716', 'abnormal placenta labyrinth morphology'), ('MP:0005031', 'abnormal trophoblast layer morphology')]
MGI:2141165 Kpna7 []
MGI:98812 Tpmt [('MP:0002429', 'abnormal blood cell morphology/development'), ('MP:0005389', 'reproductive system phenotype')]
MGI:3651973 Gm14101 []
MGI:109610 Enc1 [('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('HP:0008887', None), ('MP:0010168', 'increased CD4-positive, CD25-positive, alpha-beta regulatory T cell number'), ('HP:0008165', None), ('HP:0003233', None), ('HP:0003146', None)]
MGI:3651784 Gm11878 []
MGI:3704291 B230322F03Rik []
MGI:1913378 Mrps36 []
MGI:102501 mt-Cytb []
MGI:3704311 Gm10284 []
MGI:1919193 Tom1l1 []
MGI:95499 Fcgr2b [('MP:0005596', 'increased susceptibility to type I hypersensitivity reaction'), ('GO:0007166PHENOTYPE', None), ('HP:0000123', None), ('HP:0001733', None), ('HP:0004325', None), ('GO:0009617PHENOTYPE', None), ('HP:0000969', None), ('MP:0005026', 'decreased susceptibility to parasitic infection'), ('HP:0012649', None), ('HP:0002090', None), ('MP:0005390', 'skeleton phenotype'), ('HP:0000093', None), ('MP:0005325', 'abnormal renal glomerulus morphology'), ('MP:0001505', 'hunched posture'), ('GO:0006955PHENOTYPE', None), ('MP:0008501', 'increased IgG2b level'), ('MP:0003724', 'increased susceptibility to induced arthritis'), ('MP:0004829', 'increased anti-chromatin antibody level'), ('HP:0002633', None), ('MP:0002148', 'abnormal hypersensitivity reaction'), ('MP:0008783', 'decreased B cell apoptosis'), ('GO:0006952PHENOTYPE', None)]
MGI:3782952 Rpl15-ps3 []
ENSEMBL:ENSMUSG00000097394 AC109138.2 []
MGI:3644020 Rpl7a-ps11 []
MGI:1101055 Ifit3 []
MGI:1916804 Klhdc2 [('HP:0007957', None), ('MP:0011953', 'prolonged PQ interval'), ('HP:0004324', None), ('MP:0000443', 'abnormal snout morphology'), ('MP:0001402', 'hypoactivity'), ('HP:0001337', None), ('HP:0001640', None), ('MP:0011100', 'preweaning lethality, complete penetrance')]
MGI:1352481 Znhit2 []
MGI:3643471 Gm7493 []
MGI:3650525 Gm11737 []
MGI:3819536 Snord45c []
MGI:3651086 Gm13009 []
MGI:5455417 Gm25640 []
MGI:1336161 Klrc1 [('MP:0008561', 'decreased tumor necrosis factor secretion'), ('HP:0002090', None), ('HP:0012115', None)]
MGI:1099443 Nnmt []
MGI:3643814 Gm4841 []
MGI:1914533 Slc25a19 [('HP:0012379', None), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0012677', 'absent brain ventricles'), ('MP:0001698', 'decreased embryo size')]
MGI:1913614 2310009A05Rik []
MGI:3702070 Gm13697 []
MGI:5434024 Gm21860 []
MGI:3652217 Gm14130 []
MGI:5452525 Gm22748 []
MGI:3643433 Gm8062 []
MGI:1933177 Pik3ap1 [('MP:0008497', 'decreased IgG2b level'), ('HP:0006270', None), ('HP:0010976', None), ('HP:0004313', None), ('HP:0002850', None), ('MP:0008174', 'decreased follicular B cell number')]
MGI:1923245 5033417F24Rik []
MGI:1338798 S100a11 []
MGI:96963 Mep1a [('MP:0001935', 'decreased litter size'), ('HP:0100577', None), ('MP:0008641', 'increased circulating interleukin-1 beta level'), ('MP:0002703', 'abnormal renal tubule morphology'), ('MP:0009642', 'abnormal blood homeostasis'), ('MP:0008640', 'abnormal circulating interleukin-1 beta level'), ('MP:0008721', 'abnormal chemokine level'), ('MP:0008537', 'increased susceptibility to induced colitis')]
MGI:1915756 Ctnnbip1 [('HP:0030769', None), ('GO:0001658PHENOTYPE', None), ('MP:0010983', 'abnormal ureteric bud invasion'), ('HP:0000175', None), ('HP:0000104', None), ('MP:0001648', 'abnormal apoptosis'), ('MP:0004936', 'impaired branching involved in ureteric bud morphogenesis'), ('HP:0005105', None)]
MGI:3044162 Rsl1 []
MGI:109336 Etv6 [('GO:0022008PHENOTYPE', None), ('MP:0002083', 'premature death'), ('MP:0000245', 'abnormal erythropoiesis'), ('MP:0002401', 'abnormal lymphopoiesis'), ('HP:0001508', None), ('MP:0002082', 'postnatal lethality'), ('MP:0010373', 'myeloid hyperplasia'), ('HP:0010976', None), ('MP:0001884', 'mammary gland alveolar hyperplasia'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('HP:0001875', None), ('MP:0010299', 'increased mammary gland tumor incidence'), ('HP:0005506', None), ('HP:0001873', None), ('MP:0002414', 'abnormal myeloblast morphology/development'), ('MP:0006410', 'abnormal common myeloid progenitor cell morphology'), ('MP:0000229', 'abnormal megakaryocyte differentiation'), ('MP:0002123', 'abnormal definitive hematopoiesis'), ('HP:0002664', None)]
MGI:3704317 Sap18b []
MGI:3574098 Xlr4a []
MGI:5454748 Gm24971 []
MGI:2685663 Rbm44 [('MP:0001934', 'increased litter size')]
MGI:2136976 Cecr5 []
MGI:3643894 Gm7091 []
MGI:1347023 Txnrd2 [('HP:0000969', None), ('HP:0001392', None), ('MP:0003140', 'dilated heart atrium'), ('GO:0007507PHENOTYPE', None), ('HP:0001635', None), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('MP:0002123', 'abnormal definitive hematopoiesis'), ('MP:0002169', 'no abnormal phenotype detected'), ('GO:0030097PHENOTYPE', None)]
MGI:3781073 Gm2895 []
MGI:3648068 Gm6091 []
MGI:3648656 Gm5507 []
MGI:3651237 Gm13767 []
MGI:1913741 Polr2l []
MGI:108100 Dvl3 [('HP:0000126', None), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0011666', 'double outlet right ventricle, ventricular defect committed to aorta'), ('HP:0100891', None), ('HP:0002098', None)]
MGI:5453866 Gm24089 []
MGI:3644269 C1s2 []
MGI:3651485 Gm12583 []
MGI:96269 Ranbp1 [('HP:0008669', None), ('HP:0001508', None), ('MP:0005389', 'reproductive system phenotype')]
MGI:1276109 Cldn1 [('MP:0011100', 'preweaning lethality, complete penetrance'), ('HP:0000962', None), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('HP:0100678', None), ('HP:0001944', None)]
MGI:1261428 Chchd2 []
MGI:3802001 Gm16124 []
MGI:1925188 Fam53b [('HP:0005518', None), ('MP:0010087', 'increased circulating fructosamine level'), ('MP:0010068', 'decreased red blood cell distribution width')]
MGI:1924139 Coq8b []
MGI:1915881 Cryl1 []
MGI:1916476 Snx24 []
MGI:1918355 Mterf4 [('MP:0000278', 'abnormal myocardial fiber morphology'), ('MP:0011389', 'absent optic disc'), ('HP:0004325', None), ('MP:0001698', 'decreased embryo size'), ('HP:0001640', None), ('MP:0004215', 'abnormal myocardial fiber physiology'), ('GO:0007507PHENOTYPE', None), ('MP:0006036', 'abnormal mitochondrial physiology')]
MGI:2159681 Timd2 [('HP:0100827', None)]
MGI:1924270 Atl3 []
MGI:1919602 Zfyve27 []
MGI:3644570 Gm8724 []
MGI:96534 Cd74 [('MP:0005616', 'decreased susceptibility to type IV hypersensitivity reaction'), ('MP:0005465', 'abnormal T-helper 1 physiology'), ('MP:0008567', 'decreased interferon-gamma secretion'), ('GO:0016064PHENOTYPE', None), ('MP:0005387', 'immune system phenotype'), ('MP:0004919', 'abnormal positive T cell selection'), ('MP:0005348', 'increased T cell proliferation'), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0001265', 'decreased body size'), ('GO:0006886PHENOTYPE', None), ('MP:0005094', 'abnormal T cell proliferation'), ('MP:0002452', 'abnormal professional antigen presenting cell physiology'), ('MP:0004800', 'decreased susceptibility to experimental autoimmune encephalomyelitis'), ('MP:0005042', 'abnormal level of surface class II molecules'), ('HP:0002846', None), ('GO:0019882PHENOTYPE', None), ('MP:0001835', 'abnormal antigen presentation'), ('HP:0010976', None), ('MP:0004799', 'increased susceptibility to experimental autoimmune encephalomyelitis')]
MGI:3649557 Gm12366 []
MGI:1913844 Uqcr11 []
MGI:1338074 Ikbkg [('MP:0005343', 'increased circulating aspartate transaminase level'), ('MP:0009580', 'increased keratinocyte apoptosis'), ('HP:0000962', None), ('HP:0002605', None), ('HP:0200042', None), ('HP:0001397', None), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('HP:0011115', None), ('MP:0005387', 'immune system phenotype'), ('HP:0001392', None), ('MP:0003638', 'abnormal response/metabolism to endogenous compounds'), ('MP:0008670', 'decreased interleukin-12b secretion'), ('HP:0012379', None), ('HP:0005415', None), ('MP:0011009', 'increased circulating glutamate dehydrogenase level'), ('MP:0008734', 'decreased susceptibility to endotoxin shock'), ('MP:0005560', 'decreased circulating glucose level'), ('MP:0002941', 'increased circulating alanine transaminase level'), ('MP:0009766', 'increased sensitivity to xenobiotic induced morbidity/mortality'), ('MP:0000607', 'abnormal hepatocyte morphology'), ('HP:0012115', None), ('MP:0002161', 'abnormal fertility/fecundity'), ('MP:0008174', 'decreased follicular B cell number'), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0008873', 'increased physiological sensitivity to xenobiotic'), ('MP:0008735', 'increased susceptibility to endotoxin shock'), ('MP:0008553', 'increased circulating tumor necrosis factor level'), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic'), ('HP:0005403', None), ('MP:0008211', 'decreased mature B cell number'), ('MP:0003887', 'increased hepatocyte apoptosis'), ('MP:0009385', 'abnormal dermal pigmentation'), ('MP:0001233', 'abnormal epidermis suprabasal layer morphology'), ('HP:0002037', None), ('HP:0002846', None), ('HP:0001337', None), ('HP:0011123', None), ('MP:0006042', 'increased apoptosis'), ('MP:0008561', 'decreased tumor necrosis factor secretion'), ('MP:0008215', 'decreased immature B cell number'), ('MP:0000692', 'small spleen')]
MGI:88609 Cyp3a11 [('HP:0010683', None)]
MGI:3819548 Snord66 []
MGI:1921373 Foxp4 [('MP:0004187', 'cardia bifida'), ('MP:0006065', 'abnormal heart position or orientation'), ('MP:0002151', 'abnormal neural tube morphology'), ('HP:0001360', None), ('HP:0002414', None), ('MP:0003119', 'abnormal digestive system development'), ('GO:0007507PHENOTYPE', None), ('HP:0040006', None), ('HP:0011025', None)]
MGI:3647101 Gm16368 []
MGI:3650890 Gm13841 []
MGI:2182335 Cables2 []
MGI:2384767 BC023105 []
MGI:1914490 Taok1 []
MGI:3644593 Klhl33 []
MGI:3707464 Gm15371 []
MGI:3652108 Zeb2os []
MGI:3650480 Rpl36-ps8 []
MGI:5455269 Gm25492 []
MGI:1924105 Slc17a5 [('HP:0001508', None), ('MP:0011085', 'postnatal lethality, complete penetrance'), ('HP:0012638', None), ('HP:0002069', None), ('HP:0012757', None), ('HP:0001093', None), ('HP:0001337', None), ('MP:0009358', 'environmentally induced seizures'), ('MP:0000746', 'weakness'), ('MP:0003871', 'abnormal myelin sheath morphology'), ('HP:0000365', None), ('MP:0008025', 'brain vacuoles'), ('HP:0001288', None), ('MP:0000358', 'abnormal cell morphology'), ('HP:0000833', None)]
MGI:1914724 Snap29 [('MP:0003960', 'increased lean body mass'), ('MP:0020352', 'abnormal endoplasmic reticulum physiology'), ('HP:0011121', None), ('MP:0001239', 'abnormal epidermis stratum granulosum morphology'), ('MP:0009608', 'abnormal epidermal lamellar body morphology'), ('HP:0000962', None), ('MP:0009546', 'absent gastric milk in neonates'), ('HP:0100679', None), ('HP:0011122', None), ('MP:0010123', 'increased bone mineral content'), ('MP:0001240', 'abnormal epidermis stratum corneum morphology'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('MP:0009583', 'increased keratinocyte proliferation'), ('MP:0002656', 'abnormal keratinocyte differentiation')]
MGI:106402 Tmem30a [('MP:0011100', 'preweaning lethality, complete penetrance')]
MGI:2159680 Havcr1 [('MP:0001844', 'autoimmune response'), ('MP:0005387', 'immune system phenotype'), ('MP:0005090', 'increased double-negative T cell number'), ('MP:0005463', 'abnormal CD4-positive, alpha-beta T cell physiology'), ('MP:0008702', 'increased interleukin-5 secretion'), ('MP:0008660', 'increased interleukin-10 secretion'), ('MP:0002405', 'respiratory system inflammation'), ('MP:0002376', 'abnormal dendritic cell physiology'), ('HP:0001744', None)]
MGI:5452582 Gm22805 []
MGI:3647980 Gm5560 []
MGI:1202880 Traf4 [('MP:0011101', 'prenatal lethality, incomplete penetrance'), ('HP:0004325', None), ('MP:0003120', 'abnormal tracheal cartilage morphology'), ('GO:0030323PHENOTYPE', None), ('MP:0003091', 'abnormal cell migration'), ('MP:0000141', 'abnormal vertebral body morphology'), ('HP:0002090', None), ('MP:0001935', 'decreased litter size'), ('MP:0008146', 'asymmetric sternocostal joints'), ('MP:0004703', 'abnormal vertebral column morphology'), ('MP:0012547', 'spina bifida cystica'), ('HP:0000902', None), ('MP:0001950', 'abnormal respiratory sounds'), ('HP:0010307', None), ('GO:0007585PHENOTYPE', None), ('MP:0003051', 'curly tail'), ('HP:0002777', None), ('MP:0002376', 'abnormal dendritic cell physiology'), ('MP:0008148', 'abnormal sternocostal joint morphology'), ('MP:0001258', 'decreased body length'), ('MP:0004552', 'fused tracheal cartilage rings'), ('MP:0004173', 'abnormal intervertebral disk morphology'), ('HP:0002751', None)]
MGI:3842428 Pisd-ps1 []
MGI:4421985 n-R5s129 []
MGI:1915541 Mto1 [('HP:0001678', None), ('HP:0004325', None), ('MP:0001489', 'decreased startle reflex'), ('MP:0010632', 'cardiac muscle necrosis'), ('MP:0010394', 'decreased QRS amplitude'), ('MP:0003461', 'abnormal response to novel object'), ('MP:0001405', 'impaired coordination'), ('HP:0000752', None), ('MP:0002833', 'increased heart weight'), ('HP:0006682', None), ('MP:0011639', 'decreased mitochondrial DNA content')]
MGI:4421989 n-R5s133 []
MGI:1923875 Hoxd3os1 []
MGI:3782916 Mup-ps4 []
MGI:105061 Clcn2 [('MP:0001154', 'seminiferous tubule degeneration'), ('HP:0012863', None), ('MP:0004109', 'abnormal Sertoli cell development'), ('HP:0008669', None), ('MP:0005310', 'abnormal salivary gland physiology'), ('MP:0004022', 'abnormal cone electrophysiology'), ('MP:0008586', 'disorganized photoreceptor outer segment'), ('HP:0003134', None), ('HP:0012373', None), ('MP:0005201', 'abnormal retinal pigment epithelium morphology'), ('MP:0003871', 'abnormal myelin sheath morphology'), ('MP:0008025', 'brain vacuoles'), ('MP:0004021', 'abnormal rod electrophysiology'), ('MP:0008581', 'disorganized photoreceptor inner segment'), ('HP:0010791', None), ('MP:0003731', 'abnormal retinal outer nuclear layer morphology'), ('MP:0003690', 'abnormal glial cell physiology'), ('HP:0002143', None), ('HP:0006989', None), ('HP:0002363', None), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0003729', 'abnormal photoreceptor outer segment morphology'), ('HP:0002180', None), ('MP:0008917', 'abnormal oligodendrocyte physiology'), ('HP:0000546', None)]
MGI:5452356 Gm22579 []
MGI:5451854 Gm22077 []
MGI:2145043 Cep170b []
MGI:107784 Zfp74 []
MGI:1346875 Map3k4 [('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('HP:0003251', None), ('HP:0012861', None), ('HP:0000969', None), ('MP:0011099', 'lethality throughout fetal growth and development, complete penetrance'), ('MP:0002151', 'abnormal neural tube morphology'), ('HP:0000921', None), ('HP:0002414', None), ('MP:0003830', 'abnormal testis development'), ('MP:0001265', 'decreased body size'), ('MP:0002211', 'abnormal primary sex determination'), ('MP:0002995', 'primary sex reversal'), ('MP:0006418', 'abnormal testis cord formation'), ('GO:0010468PHENOTYPE', None)]
MGI:3650297 Gm11967 []
MGI:97876 Rbp1 [('MP:0005551', 'abnormal eye electrophysiology'), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('MP:0005444', 'abnormal retinol metabolism'), ('MP:0003011', 'delayed dark adaptation'), ('MP:0011750', 'abnormal seminiferous tubule epithelium morphology'), ('HP:0012373', None), ('MP:0011234', 'abnormal retinol level'), ('HP:0007973', None)]
MGI:4422058 n-R5s193 []
MGI:2443935 Snapc4 [('MP:0005296', 'abnormal humerus morphology')]
MGI:1925300 4930570N18Rik []
MGI:2182838 Serpina3f []
MGI:1917115 A1cf [('MP:0000352', 'decreased cell proliferation'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance')]
MGI:3705193 Gm15265 []
MGI:3645107 Gm6506 []
MGI:2180021 Cd207 [('MP:0005616', 'decreased susceptibility to type IV hypersensitivity reaction'), ('MP:0008496', 'decreased IgG2a level'), ('MP:0005387', 'immune system phenotype'), ('HP:0002850', None), ('MP:0008498', 'decreased IgG3 level'), ('HP:0002720', None), ('MP:0008127', 'decreased dendritic cell number'), ('MP:0000696', "abnormal Peyer's patch morphology"), ('MP:0008118', 'absent Langerhans cell')]
MGI:1098791 Sepsecs []
MGI:1861694 Cited4 []
MGI:3819500 Snora31 []
MGI:4937017 Gm17383 []
MGI:3783088 Gm15644 []
MGI:2181434 Phc3 []
MGI:4421968 n-R5s118 []
MGI:3646919 Fignl2 []
MGI:2140623 Mob3c []
MGI:1859559 Slc22a7 []
MGI:3704247 Rpl35a-ps5 []
MGI:88515 Cryaa [('GO:0007017PHENOTYPE', None), ('HP:0000517', None), ('MP:0003237', 'abnormal lens epithelium morphology'), ('HP:0008063', None), ('MP:0008796', 'increased lens fiber apoptosis'), ('HP:0000568', None), ('GO:0001666PHENOTYPE', None), ('GO:0006915PHENOTYPE', None), ('GO:0010629PHENOTYPE', None), ('MP:0003236', 'abnormal lens capsule morphology'), ('GO:0001934PHENOTYPE', None)]
MGI:5531285 Gm27903 []
MGI:5453796 Gm24019 []
MGI:3646005 Gm5944 []
MGI:1925841 Mir142hg [('MP:0005461', 'abnormal dendritic cell morphology'), ('MP:0008126', 'increased dendritic cell number'), ('MP:0008115', 'abnormal dendritic cell differentiation')]
MGI:88057 Apoe [('MP:0003949', 'abnormal circulating lipid level'), ('MP:0004404', 'cochlear outer hair cell degeneration'), ('MP:0002270', 'abnormal pulmonary alveolus morphology'), ('MP:0005146', 'decreased circulating VLDL cholesterol level'), ('HP:0006482', None), ('MP:0009289', 'decreased epididymal fat pad weight'), ('MP:0001663', 'abnormal digestive system physiology'), ('MP:0001413', 'abnormal response to new environment'), ('HP:0012153', None), ('MP:0000788', 'abnormal cerebral cortex morphology'), ('HP:0002910', None), ('MP:0001392', 'abnormal locomotor behavior'), ('MP:0001745', 'increased circulating corticosterone level'), ('MP:0005341', 'decreased susceptibility to atherosclerosis'), ('HP:0002621', None), ('MP:0010026', 'decreased liver cholesterol level'), ('HP:0008216', None), ('MP:0005455', 'increased susceptibility to weight gain'), ('MP:0010451', 'kidney microaneurysm'), ('MP:0008288', 'abnormal adrenal cortex morphology'), ('MP:0005591', 'decreased vasodilation'), ('MP:0004398', 'cochlear inner hair cell degeneration'), ('MP:0001363', 'increased anxiety-related response'), ('MP:0001625', 'cardiac hypertrophy'), ('MP:0002891', 'increased insulin sensitivity'), ('HP:0002904', None), ('MP:0004148', 'increased compact bone thickness'), ('MP:0005167', 'abnormal blood-brain barrier function'), ('MP:0003871', 'abnormal myelin sheath morphology'), ('MP:0003674', 'oxidative stress'), ('MP:0005325', 'abnormal renal glomerulus morphology'), ('MP:0010895', 'increased lung compliance'), ('HP:0003233', None), ('MP:0002733', 'abnormal thermal nociception'), ('MP:0004879', 'decreased systemic vascular resistance'), ('MP:0008289', 'abnormal adrenal medulla morphology'), ('GO:0042157PHENOTYPE', None), ('HP:0002718', None), ('MP:0004952', 'increased spleen weight'), ('MP:0005412', 'vascular stenosis'), ('MP:0000181', 'abnormal circulating LDL cholesterol level'), ('MP:0001212', 'skin lesions'), ('MP:0001417', 'decreased exploration in new environment'), ('HP:0003330', None), ('HP:0003141', None), ('HP:0003146', None), ('MP:0005459', 'decreased percent body fat/body weight'), ('HP:0012184', None), ('HP:0012757', None), ('MP:0001547', 'abnormal lipid level'), ('HP:0011001', None), ('MP:0008182', 'decreased marginal zone B cell number'), ('HP:0002155', None), ('MP:0004630', 'spiral modiolar artery stenosis'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0008916', 'abnormal astrocyte physiology'), ('MP:0002376', 'abnormal dendritic cell physiology'), ('MP:0008174', 'decreased follicular B cell number'), ('MP:0005551', 'abnormal eye electrophysiology'), ('GO:0006979PHENOTYPE', None), ('HP:0002486', None), ('MP:0004769', 'abnormal synaptic vesicle morphology'), ('MP:0008706', 'decreased interleukin-6 secretion'), ('MP:0004629', 'abnormal spiral modiolar artery morphology'), ('HP:0002634', None), ('HP:0003464', None), ('HP:0004325', None), ('HP:0001006', None), ('MP:0009122', 'decreased white fat cell lipid droplet size'), ('HP:0030781', None), ('MP:0008515', 'thin retinal outer nuclear layer'), ('MP:0001489', 'decreased startle reflex'), ('MP:0011508', 'glomerular capillary thrombosis'), ('MP:0010868', 'increased bone trabecula number'), ('HP:0003073', None), ('MP:0005641', 'increased mean corpuscular hemoglobin concentration'), ('MP:0003976', 'decreased circulating VLDL triglyceride level'), ('MP:0011561', 'renal glomerulus lipidosis'), ('HP:0001114', None), ('MP:0000031', 'abnormal cochlea morphology'), ('HP:0000752', None), ('MP:0002164', 'abnormal gland physiology'), ('MP:0005560', 'decreased circulating glucose level'), ('HP:0003292', None), ('MP:0002421', 'abnormal cell-mediated immunity'), ('GO:0008203PHENOTYPE', None), ('HP:0010702', None), ('MP:0001942', 'abnormal lung volume'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0000043', 'organ of Corti degeneration'), ('MP:0011309', 'abnormal kidney arterial blood vessel morphology'), ('MP:0006076', 'abnormal circulating homocysteine level'), ('HP:0002846', None), ('MP:0005317', 'increased triglyceride level'), ('MP:0011941', 'increased fluid intake')]
MGI:2686991 Tmem120a []
MGI:3026615 Mettl7a2 []
MGI:3650055 Gm11972 []
MGI:3781847 Gm3671 []
MGI:3704266 Gm10243 []
MGI:4422029 n-R5s165 []
MGI:3652005 Gm12918 []
MGI:3819484 Scarna2 []
MGI:1923839 Unc5cl []
MGI:4421886 n-R5s41 []
MGI:2145733 Cphx1 []
MGI:104913 Abi1 [('MP:0003229', 'abnormal vitelline vasculature morphology'), ('HP:0030769', None), ('MP:0001787', 'pericardial edema'), ('MP:0010545', 'abnormal heart layer morphology'), ('MP:0002836', 'abnormal chorion morphology'), ('MP:0001622', 'abnormal vasculogenesis'), ('MP:0003974', 'abnormal endocardium morphology'), ('MP:0000930', 'wavy neural tube')]
MGI:2685587 Vmo1 []
MGI:3619412 Mir451a [('HP:0001871', None), ('MP:0000693', 'spleen hyperplasia'), ('MP:0000245', 'abnormal erythropoiesis'), ('HP:0001903', None), ('MP:0008406', 'increased cellular sensitivity to hydrogen peroxide'), ('HP:0012517', None), ('HP:0011273', None), ('MP:0005097', 'polychromatophilia'), ('MP:0004952', 'increased spleen weight'), ('MP:0011241', 'abnormal fetal derived definitive erythrocyte cell number'), ('MP:0001586', 'abnormal erythrocyte cell number')]
MGI:3819504 Snora36b []
MGI:1313259 Slfn1 [('MP:0002169', 'no abnormal phenotype detected'), ('MP:0005387', 'immune system phenotype')]
MGI:3645365 Gm9104 []
MGI:3030535 Olfr701 []
MGI:3650107 Gm14279 []
MGI:3782703 Gm4518 []
MGI:1924242 Fam219aos []
MGI:3705781 Rpl39-ps []
MGI:1099464 Ssr4 []
MGI:2385708 Rin3 []
MGI:95392 Eng [('MP:0000267', 'abnormal heart development'), ('MP:0002083', 'premature death'), ('MP:0001719', 'absent vitelline blood vessels'), ('MP:0012732', 'abnormal perineural vascular plexus morphology'), ('MP:0004086', 'absent heartbeat'), ('MP:0005602', 'decreased angiogenesis'), ('HP:0000252', None), ('MP:0004177', 'tail telangiectases'), ('GO:0003273PHENOTYPE', None), ('HP:0001698', None), ('MP:0003396', 'abnormal embryonic hematopoiesis'), ('HP:0011854', None), ('HP:0002098', None), ('HP:0001640', None), ('MP:0001689', 'incomplete somite formation'), ('HP:0001892', None), ('MP:0000265', 'atretic vasculature'), ('MP:0000259', 'abnormal vascular development'), ('MP:0000420', 'ruffled hair'), ('MP:0000269', 'abnormal heart looping'), ('HP:0002629', None), ('MP:0013241', 'embryo tissue necrosis'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0013139', 'moribund'), ('MP:0004787', 'abnormal dorsal aorta morphology'), ('MP:0004883', 'abnormal vascular wound healing'), ('HP:0100026', None), ('HP:0011029', None), ('GO:0001525PHENOTYPE', None), ('MP:0002652', 'thin myocardium')]
MGI:4421955 n-R5s107 []
MGI:2136335 Klhl1 [('HP:0001288', None), ('MP:0000890', 'thin cerebellar molecular layer')]
MGI:2139790 Clca4b []
MGI:1916990 Dph5 []
MGI:95561 Flt4 [('HP:0045006', None), ('MP:0002082', 'postnatal lethality'), ('MP:0005385', 'cardiovascular system phenotype'), ('MP:0001787', 'pericardial edema'), ('MP:0004076', 'abnormal vitelline vascular remodeling'), ('GO:0007585PHENOTYPE', None), ('MP:0003659', 'abnormal lymph circulation'), ('MP:0012732', 'abnormal perineural vascular plexus morphology'), ('HP:0002597', None), ('MP:0000013', 'abnormal adipose tissue distribution'), ('MP:0004784', 'abnormal anterior cardinal vein morphology'), ('GO:0003016PHENOTYPE', None), ('MP:0005389', 'reproductive system phenotype')]
MGI:97771 Proc [('MP:0001656', 'focal hepatic necrosis'), ('HP:0001395', None), ('HP:0002170', None), ('GO:0007596PHENOTYPE', None), ('GO:0001889PHENOTYPE', None), ('MP:0011089', 'perinatal lethality, complete penetrance'), ('MP:0002551', 'abnormal blood coagulation'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('HP:0002033', None), ('MP:0005048', 'abnormal thrombosis'), ('MP:0002419', 'abnormal innate immunity'), ('HP:0000978', None)]
MGI:4936934 Gm17300 []
MGI:1925182 Sh2d4b []
MGI:5453656 Gm23879 []
MGI:1916780 2310002L09Rik []
MGI:2146198 Ttc38 []
MGI:1925246 Cutal [('HP:0010442', None)]
MGI:3647437 Rps11-ps5 []
MGI:5504031 Gm26916 []
MGI:2385205 Ipo13 []
MGI:1915646 Nat8 []
MGI:3651234 Rpsa-ps12 []
MGI:3645153 Gm7389 []
MGI:3650208 Gm13167 []
MGI:3605234 BC049715 []
MGI:3704353 Gm10145 []
MGI:2443267 Agfg2 []
MGI:1341830 Eif2ak3 [('MP:0005215', 'abnormal pancreatic islet morphology'), ('MP:0009176', 'increased pancreatic alpha cell number'), ('MP:0009145', 'abnormal pancreatic acinus morphology'), ('MP:0000141', 'abnormal vertebral body morphology'), ('MP:0004986', 'abnormal osteoblast morphology'), ('MP:0011085', 'postnatal lethality, complete penetrance'), ('HP:0011842', None), ('MP:0001402', 'hypoactivity'), ('GO:0031016PHENOTYPE', None), ('MP:0001505', 'hunched posture'), ('MP:0005220', 'abnormal exocrine pancreas morphology'), ('MP:0003562', 'abnormal pancreatic beta cell physiology'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('GO:0004672PHENOTYPE', None), ('HP:0003074', None), ('HP:0009121', None), ('HP:0006568', None), ('HP:0002753', None), ('MP:0009174', 'absent pancreatic beta cells'), ('MP:0003564', 'abnormal insulin secretion'), ('HP:0000833', None), ('MP:0008873', 'increased physiological sensitivity to xenobiotic'), ('HP:0000939', None), ('MP:0020137', 'decreased bone mineralization'), ('MP:0009172', 'small pancreatic islets'), ('MP:0009164', 'exocrine pancreas atrophy'), ('MP:0003340', 'acute pancreas inflammation'), ('GO:0019722PHENOTYPE', None), ('HP:0003103', None)]
MGI:4937850 Gm17023 []
MGI:3643510 Gm8807 []
MGI:1353496 Slc25a5 []
MGI:3819521 Snord13 []
MGI:5433902 Gm21738 []
MGI:96244 Hspa1a []
MGI:1919016 Raver1 [('MP:0001475', 'reduced long term depression'), ('MP:0005384', 'cellular phenotype')]
MGI:3649470 Gm13292 []
MGI:3805955 Gm6043 []
MGI:3651428 Gm13522 []
MGI:102503 mt-Co2 []
MGI:3644689 Gm8623 []
MGI:1858496 Deaf1 [('HP:0030769', None), ('MP:0003012', 'no phenotypic analysis'), ('MP:0000929', 'open neural tube'), ('MP:0003460', 'decreased fear-related response'), ('HP:0000892', None), ('HP:0000902', None)]
MGI:2448403 Hist1h2bl []
MGI:2684919 Gcnt4 [('GO:0048872PHENOTYPE', None), ('MP:0005478', 'decreased circulating thyroxine level'), ('GO:0048729PHENOTYPE', None), ('MP:0000219', 'increased neutrophil cell number')]
MGI:3650675 Rpl9-ps3 []
MGI:1923639 Ankrd36 []
MGI:3647829 Gm10382 []
MGI:1916946 2310057N15Rik []
MGI:3603817 Appbp2os []
MGI:3780066 Gm9658 []
MGI:1931086 Tusc2 [('HP:0001974', None), ('HP:0004325', None), ('GO:0006909PHENOTYPE', None), ('HP:0012089', None), ('MP:0002019', 'abnormal tumor incidence'), ('MP:0011508', 'glomerular capillary thrombosis'), ('GO:2000377PHENOTYPE', None), ('MP:0013249', 'adipose tissue necrosis'), ('GO:0032618PHENOTYPE', None), ('MP:0004794', 'increased anti-nuclear antigen antibody level'), ('MP:0005325', 'abnormal renal glomerulus morphology'), ('HP:0002665', None), ('MP:0008676', 'decreased interleukin-15 secretion'), ('GO:0048469PHENOTYPE', None), ('HP:0002633', None)]
MGI:1920949 Ppp2r1b [('HP:0003251', None), ('MP:0000219', 'increased neutrophil cell number')]
MGI:5451833 Gm22056 []
MGI:3650059 Snhg15 []
MGI:3040693 Zmiz1 [('GO:0003007PHENOTYPE', None), ('MP:0002053', 'decreased incidence of induced tumors'), ('MP:0004787', 'abnormal dorsal aorta morphology'), ('HP:0005403', None), ('HP:0000980', None), ('MP:0002672', 'abnormal pharyngeal arch artery morphology'), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0012732', 'abnormal perineural vascular plexus morphology'), ('MP:0001722', 'pale yolk sac'), ('GO:0001570PHENOTYPE', None), ('GO:0048589PHENOTYPE', None), ('MP:0014224', 'increased ileal goblet cell number'), ('MP:0001698', 'decreased embryo size'), ('HP:0040218', None), ('MP:0000285', 'abnormal heart valve morphology'), ('GO:0001701PHENOTYPE', None), ('MP:0003105', 'abnormal heart atrium morphology'), ('HP:0010976', None), ('GO:0007296PHENOTYPE', None)]
MGI:1917004 Fbxo7 [('MP:0002875', 'decreased erythrocyte cell number'), ('HP:0005518', None), ('HP:0002904', None), ('HP:0003251', None), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('MP:0002416', 'abnormal proerythroblast morphology'), ('HP:0001894', None), ('GO:0045620PHENOTYPE', None), ('MP:0010088', 'decreased circulating fructosamine level'), ('MP:0011179', 'decreased erythroblast number'), ('HP:0012132', None), ('MP:0010850', 'increased effector memory CD8-positive, alpha-beta T cell number'), ('MP:0005561', 'increased mean corpuscular hemoglobin')]
MGI:2387894 Snord87 []
MGI:1917685 Inf2 []
MGI:5434641 Gm21286 []
MGI:108093 Bid [('HP:0025100', None), ('MP:0003992', 'increased mortality induced by ionizing radiation'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0005370', 'liver/biliary system phenotype'), ('MP:0008734', 'decreased susceptibility to endotoxin shock'), ('MP:0002273', 'abnormal pulmonary alveolus epithelial cell morphology'), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality')]
MGI:5455942 Gm26165 []
MGI:3037684 Gm1826 []
MGI:5452563 Gm22786 []
MGI:2144284 Inca1 [('MP:0002357', 'abnormal spleen white pulp morphology'), ('HP:0005513', None), ('HP:0003261', None), ('MP:0011704', 'decreased fibroblast proliferation'), ('HP:0001978', None)]
MGI:3801722 Gm15982 []
MGI:3648580 Gm5863 []
MGI:1926005 Fam122b []
MGI:1916027 Tmem53 []
MGI:2445363 Serpinb1c []
MGI:3649405 Cd300ld2 []
MGI:1345092 Pglyrp1 [('GO:0050728PHENOTYPE', None), ('HP:0011990', None), ('MP:0000488', 'abnormal intestinal epithelium morphology'), ('MP:0000490', 'abnormal crypts of Lieberkuhn morphology'), ('MP:0000512', 'intestinal ulcer'), ('MP:0005074', 'impaired granulocyte bactericidal activity'), ('MP:0003449', 'abnormal intestinal goblet cell morphology'), ('MP:0003724', 'increased susceptibility to induced arthritis'), ('MP:0000511', 'abnormal intestinal mucosa morphology'), ('MP:0010377', 'abnormal gut flora balance'), ('MP:0008705', 'increased interleukin-6 secretion'), ('HP:0001824', None), ('HP:0002718', None), ('MP:0008537', 'increased susceptibility to induced colitis'), ('MP:0009788', 'increased susceptibility to bacterial infection induced morbidity/mortality'), ('HP:0011115', None)]
MGI:4439785 Gm16861 []
MGI:103575 Skp1a []
MGI:4421952 n-R5s104 []
MGI:3649802 Rps11-ps3 []
MGI:3650081 Rpl36-ps2 []
MGI:3704493 Gm10053 []
MGI:1201409 Pknox1 [('HP:0001903', None), ('HP:0000969', None), ('MP:0004196', 'abnormal prenatal growth/weight/body size'), ('MP:0005090', 'increased double-negative T cell number'), ('MP:0011096', 'embryonic lethality between implantation and somite formation, complete penetrance'), ('MP:0005391', 'vision/eye phenotype'), ('GO:0043010PHENOTYPE', None), ('MP:0006413', 'increased T cell apoptosis'), ('MP:0001693', 'failure of primitive streak formation'), ('HP:0010976', None), ('MP:0002429', 'abnormal blood cell morphology/development'), ('HP:0000708', None), ('HP:0012153', None), ('HP:0000980', None), ('MP:0009703', 'decreased birth body size'), ('MP:0001698', 'decreased embryo size'), ('MP:0008083', 'decreased single-positive T cell number'), ('MP:0011089', 'perinatal lethality, complete penetrance'), ('MP:0001672', 'abnormal embryo development'), ('GO:0030217PHENOTYPE', None), ('MP:0003984', 'embryonic growth retardation'), ('MP:0008211', 'decreased mature B cell number'), ('MP:0008209', 'decreased pre-B cell number'), ('GO:0001525PHENOTYPE', None), ('GO:0030097PHENOTYPE', None)]
MGI:3652099 Gm11586 []
MGI:1914267 Fam210b []
MGI:3650486 Gm13121 []
MGI:2143702 Enpp3 [('MP:0005596', 'increased susceptibility to type I hypersensitivity reaction'), ('HP:0002090', None), ('MP:0005387', 'immune system phenotype'), ('MP:0013956', 'decreased colon length'), ('HP:0100495', None), ('MP:0008699', 'increased interleukin-4 secretion'), ('MP:0002606', 'increased basophil cell number'), ('MP:0010180', 'increased susceptibility to weight loss'), ('MP:0002464', 'abnormal basophil physiology'), ('MP:0009763', 'increased sensitivity to induced morbidity/mortality')]
MGI:1891389 Ccl27b []
MGI:1918940 Esm1 []
MGI:1929711 Anapc7 []
MGI:2444345 Acnat2 []
MGI:1921285 Nol9 []
MGI:88597 Cyp2a5 []
MGI:1923151 Dcp1a []
MGI:1918095 4921508D12Rik []
MGI:3606001 Apol9a []
MGI:2445168 Elmod3 []
MGI:107932 Ndufs6 [('MP:0002083', 'premature death'), ('MP:0003915', 'increased left ventricle weight'), ('GO:0006936PHENOTYPE', None), ('MP:0005281', 'increased fatty acid level'), ('MP:0001935', 'decreased litter size'), ('MP:0003819', 'increased left ventricle diastolic pressure'), ('MP:0003913', 'increased heart right ventricle weight'), ('MP:0003141', 'cardiac fibrosis'), ('MP:0004084', 'abnormal cardiac muscle relaxation'), ('MP:0010563', 'increased heart right ventricle size'), ('GO:0006631PHENOTYPE', None), ('MP:0008772', 'increased heart ventricle size'), ('GO:0072358PHENOTYPE', None), ('MP:0005598', 'decreased ventricle muscle contractility'), ('MP:0010579', 'increased heart left ventricle size'), ('HP:0008322', None), ('MP:0003822', 'decreased left ventricle systolic pressure'), ('HP:0001824', None), ('MP:0011631', 'decreased mitochondria size')]
MGI:3646431 Rpl21-ps3 []
MGI:1916998 Aldh16a1 [('HP:0003072', None), ('HP:0002902', None), ('HP:0007973', None)]
MGI:5454744 Gm24967 []
MGI:2141980 Scaf1 []
MGI:1926230 Treh [('MP:0005367', 'renal/urinary system phenotype')]
MGI:3649808 Gm11703 []
MGI:96551 Il2rg [('MP:0000693', 'spleen hyperplasia'), ('MP:0000494', 'abnormal cecum morphology'), ('MP:0002217', 'small lymph nodes'), ('MP:0002416', 'abnormal proerythroblast morphology'), ('MP:0002378', 'abnormal gut-associated lymphoid tissue morphology'), ('MP:0000706', 'small thymus'), ('MP:0008479', 'decreased spleen white pulp amount'), ('MP:0008700', 'decreased interleukin-4 secretion'), ('MP:0005092', 'decreased double-positive T cell number'), ('MP:0001927', 'abnormal estrous cycle'), ('MP:0009011', 'prolonged diestrus'), ('MP:0005389', 'reproductive system phenotype'), ('MP:0013592', 'small thymus cortex'), ('HP:0011111', None), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('HP:0002850', None), ('MP:0005046', 'absent spleen white pulp'), ('HP:0004313', None), ('MP:0005425', 'increased macrophage cell number'), ('MP:0008688', 'decreased interleukin-2 secretion'), ('HP:0010987', None), ('MP:0001601', 'abnormal myelopoiesis'), ('MP:0008498', 'decreased IgG3 level'), ('HP:0010975', None), ('MP:0008348', 'absent gamma-delta T cells'), ('HP:0100494', None), ('MP:0000715', 'decreased thymocyte number'), ('MP:0008356', 'abnormal gamma-delta T cell differentiation'), ('MP:0000219', 'increased neutrophil cell number'), ('MP:0005095', 'decreased T cell proliferation'), ('MP:0012529', 'abnormal decidua basalis morphology'), ('MP:0004816', 'abnormal class switch recombination'), ('MP:0009020', 'prolonged metestrus'), ('MP:0002464', 'abnormal basophil physiology'), ('MP:0010776', 'abnormal placenta metrial gland morphology'), ('MP:0002421', 'abnormal cell-mediated immunity'), ('MP:0008122', 'decreased myeloid dendritic cell number'), ('MP:0008209', 'decreased pre-B cell number'), ('MP:0002831', "absent Peyer's patches"), ('MP:0008098', 'decreased plasma cell number')]
MGI:3603401 A230005M16Rik []
MGI:1931870 Zbtb22 []
MGI:3647234 Rps15-ps2 []
MGI:3644333 Gm8756 []
MGI:2443701 B930095G15Rik []
MGI:3645172 Gm9242 []
MGI:1918141 Spdya []
MGI:3801728 Gm16216 []
MGI:2442153 Gak [('MP:0011085', 'postnatal lethality, complete penetrance'), ('MP:0000812', 'abnormal dentate gyrus morphology'), ('MP:0010900', 'abnormal pulmonary interalveolar septum morphology'), ('MP:0010902', 'abnormal pulmonary alveolar sac morphology'), ('MP:0004948', 'abnormal neuronal precursor proliferation'), ('GO:0072583PHENOTYPE', None), ('HP:0001392', None), ('MP:0012242', 'abnormal hepatoblast differentiation'), ('MP:0004782', 'abnormal surfactant physiology'), ('MP:0008458', 'abnormal cortical ventricular zone morphology'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('HP:0011124', None), ('MP:0004981', 'decreased neuronal precursor cell number')]
MGI:3652124 Gm11793 []
MGI:88384 F9 [('MP:0002083', 'premature death'), ('HP:0003010', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0012305', 'umbilical cord hemorrhage'), ('MP:0002551', 'abnormal blood coagulation'), ('HP:0001892', None), ('MP:0005023', 'abnormal wound healing')]
MGI:3809525 Gm9574 []
MGI:4414990 Gm16570 []
MGI:103572 Cebpe [('MP:0005012', 'decreased eosinophil cell number'), ('MP:0002083', 'premature death'), ('GO:0030225PHENOTYPE', None), ('HP:0011990', None), ('HP:0001978', None), ('MP:0008111', 'abnormal granulocyte differentiation'), ('HP:0011121', None), ('MP:0001602', 'impaired myelopoiesis'), ('HP:0011992', None), ('MP:0000321', 'increased bone marrow cell number'), ('HP:0012649', None), ('MP:0000702', 'enlarged lymph nodes'), ('HP:0002754', None), ('HP:0012384', None), ('HP:0000509', None), ('MP:0008097', 'increased plasma cell number'), ('MP:0006410', 'abnormal common myeloid progenitor cell morphology')]
MGI:4421965 n-R5s115 []
MGI:2687281 Fam105a []
MGI:101782 Scnn1a [('MP:0001402', 'hypoactivity'), ('MP:0011417', 'abnormal renal transport'), ('HP:0100738', None), ('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:2140260 Pcsk9 [('HP:0003464', None), ('HP:0003146', None), ('HP:0012184', None)]
MGI:700013 Sorbs3 []
MGI:3648029 Gm7658 []
MGI:96239 Hsf2 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0001134', 'absent corpus luteum'), ('MP:0004901', 'decreased male germ cell number'), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('HP:0003228', None), ('MP:0002682', 'decreased mature ovarian follicle number'), ('HP:0001342', None), ('HP:0007082', None), ('HP:0008669', None), ('MP:0004929', 'decreased epididymis weight'), ('MP:0005431', 'decreased oocyte number'), ('MP:0001153', 'small seminiferous tubules')]
MGI:104725 Atn1 [('GO:0008340PHENOTYPE', None), ('HP:0004325', None), ('MP:0003631', 'nervous system phenotype'), ('HP:0005736', None), ('GO:0008584PHENOTYPE', None), ('HP:0008887', None), ('HP:0001627', None), ('GO:0035264PHENOTYPE', None), ('MP:0002673', 'abnormal sperm number'), ('HP:0008734', None), ('GO:0007283PHENOTYPE', None), ('HP:0001288', None), ('MP:0002169', 'no abnormal phenotype detected'), ('GO:0009791PHENOTYPE', None)]
MGI:3646182 Gm9385 []
MGI:4421733 n-R5s1 []
MGI:103009 Epb41l2 [('HP:0008669', None), ('MP:0011750', 'abnormal seminiferous tubule epithelium morphology'), ('MP:0004852', 'decreased testis weight'), ('HP:0008734', None), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:1315201 Grcc10 []
MGI:3644263 Gm8648 []
MGI:5453489 Gm23712 []
MGI:3646180 Gm6361 []
MGI:1916709 Ubl7 []
MGI:109443 Gtpbp1 []
MGI:1341171 Cnmd [('MP:0001533', 'abnormal skeleton physiology'), ('HP:0011001', None), ('MP:0005006', 'abnormal osteoblast physiology'), ('GO:0016525PHENOTYPE', None), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0010683', None)]
MGI:894407 Tmem165 [('HP:0000525', None)]
MGI:96513 Igkv15-103 []
MGI:107750 Dync1i2 [('MP:0001392', 'abnormal locomotor behavior'), ('HP:0002902', None), ('HP:0003146', None)]
MGI:5451819 Gm22042 []
MGI:3781450 Gm3272 []
MGI:4422013 n-R5s151 []
MGI:5455409 Gm25632 []
MGI:1919485 1700021J08Rik []
MGI:1352754 Clic4 [('MP:0006055', 'abnormal vascular endothelial cell morphology'), ('HP:0200042', None), ('GO:0061299PHENOTYPE', None), ('GO:0035264PHENOTYPE', None), ('MP:0008734', 'decreased susceptibility to endotoxin shock'), ('GO:0007035PHENOTYPE', None), ('MP:0009703', 'decreased birth body size'), ('MP:0008554', 'decreased circulating tumor necrosis factor level'), ('HP:0002718', None), ('MP:0008706', 'decreased interleukin-6 secretion'), ('MP:0008770', 'decreased survivor rate'), ('MP:0004003', 'abnormal vascular endothelial cell physiology'), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality'), ('HP:0011115', None)]
MGI:3644824 Gm5540 []
MGI:2142980 Tmem266 []
MGI:1336888 Prdx6b []
MGI:3652245 Gm12989 []
MGI:5455100 Gm25323 []
MGI:1913788 Rps19bp1 [('HP:0001743', None)]
MGI:2142572 Mtus1 [('MP:0002083', 'premature death'), ('HP:0001974', None), ('MP:0000688', 'lymphoid hyperplasia'), ('HP:0004324', None), ('MP:0005385', 'cardiovascular system phenotype'), ('MP:0004801', 'increased susceptibility to systemic lupus erythematosus'), ('HP:0000099', None), ('MP:0000351', 'increased cell proliferation'), ('HP:0001873', None), ('MP:0000358', 'abnormal cell morphology')]
MGI:1915244 Mrps11 []
MGI:2139258 Depdc7 []
MGI:2149905 Ugt2a1 []
MGI:1915686 Rpl34 []
MGI:4421910 n-R5s65 []
MGI:107682 Ppp1r14b []
MGI:3781346 Rpl35a-ps7 []
MGI:98105 Rps12 []
MGI:5454285 Gm24508 []
MGI:3645947 Mrip-ps []
MGI:5477264 Gm26770 []
MGI:3650759 Gm12715 []
MGI:3802118 Mup-ps13 []
MGI:1306775 Sucla2 [('MP:0011089', 'perinatal lethality, complete penetrance'), ('MP:0006036', 'abnormal mitochondrial physiology'), ('MP:0011638', 'abnormal mitochondrial chromosome morphology'), ('MP:0011639', 'decreased mitochondrial DNA content')]
MGI:1923580 Sephs1 []
MGI:1355310 Scly [('MP:0001463', 'abnormal spatial learning')]
MGI:1347354 Homer2 [('HP:0012638', None), ('MP:0009754', 'enhanced behavioral response to cocaine'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0009713', 'enhanced conditioned place preference behavior'), ('MP:0002062', 'abnormal associative learning')]
MGI:3648534 Gm8973 []
MGI:1923733 Lmf1 [('GO:0009306PHENOTYPE', None), ('MP:0002082', 'postnatal lethality'), ('HP:0000980', None), ('MP:0001402', 'hypoactivity'), ('HP:0002155', None), ('HP:0011029', None), ('HP:0003124', None), ('MP:0001182', 'lung hemorrhage'), ('HP:0000961', None)]
MGI:4937867 Gm17040 []
MGI:3644227 Gm8991 []
MGI:5451977 Gm22200 []
MGI:3645339 Gm5866 []
MGI:1861232 Clec4e [('MP:0005616', 'decreased susceptibility to type IV hypersensitivity reaction'), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic'), ('MP:0005025', 'abnormal response to infection'), ('GO:0002292PHENOTYPE', None), ('HP:0012312', None), ('MP:0004800', 'decreased susceptibility to experimental autoimmune encephalomyelitis'), ('MP:0008706', 'decreased interleukin-6 secretion'), ('MP:0008127', 'decreased dendritic cell number'), ('MP:0008615', 'decreased circulating interleukin-17 level')]
MGI:2182607 Sdsl []
MGI:88574 Cybb [('MP:0009873', 'abnormal aorta tunica media morphology'), ('MP:0002451', 'abnormal macrophage physiology'), ('MP:0001473', 'reduced long term potentiation'), ('HP:0011990', None), ('MP:0006317', 'decreased urine sodium level'), ('MP:0005385', 'cardiovascular system phenotype'), ('HP:0004325', None), ('MP:0010759', 'decreased right ventricle systolic pressure'), ('HP:0011037', None), ('MP:0003957', 'abnormal nitric oxide homeostasis'), ('HP:0002719', None), ('MP:0005381', 'digestive/alimentary phenotype'), ('MP:0002207', 'abnormal long term potentiation'), ('GO:0050665PHENOTYPE', None), ('MP:0000511', 'abnormal intestinal mucosa morphology'), ('MP:0005527', 'increased renal glomerular filtration rate'), ('MP:0003447', 'decreased tumor growth/size'), ('MP:0001405', 'impaired coordination'), ('MP:0005088', 'increased acute inflammation'), ('MP:0005526', 'decreased renal plasma flow rate'), ('HP:0012649', None)]
MGI:1344415 Dmtf1 [('HP:0001508', None), ('MP:0013320', 'dilated seminal vesicles'), ('MP:0009546', 'absent gastric milk in neonates'), ('MP:0009703', 'decreased birth body size'), ('HP:0000021', None), ('MP:0002020', 'increased tumor incidence')]
MGI:3705447 Gm15267 []
MGI:3840152 Gm16335 []
MGI:4936909 Gm17275 []
MGI:3647495 Gm7399 []
MGI:5434255 Gapdh-ps15 [('MP:0005397', 'hematopoietic system phenotype'), ('MP:0005385', 'cardiovascular system phenotype'), ('GO:0004365PHENOTYPE', None), ('MP:0002596', 'abnormal hematocrit'), ('MP:0011609', 'decreased glyceraldehyde-3-phosphate dehydrogenase (NAD+) (phosphorylating) activity'), ('HP:0012379', None), ('MP:0002874', 'decreased hemoglobin content'), ('MP:0005367', 'renal/urinary system phenotype'), ('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0008770', 'decreased survivor rate'), ('MP:0005378', 'growth/size/body region phenotype')]
MGI:2139607 Usp53 [('MP:0004749', 'nonsyndromic hearing loss'), ('MP:0004398', 'cochlear inner hair cell degeneration'), ('MP:0001489', 'decreased startle reflex'), ('MP:0002857', 'cochlear ganglion degeneration'), ('GO:0001508PHENOTYPE', None), ('GO:0007605PHENOTYPE', None), ('MP:0005386', 'behavior/neurological phenotype'), ('GO:0010996PHENOTYPE', None), ('MP:0004399', 'abnormal cochlear outer hair cell morphology'), ('GO:0006915PHENOTYPE', None), ('HP:0000365', None), ('MP:0004528', 'fused outer hair cell stereocilia'), ('MP:0004411', 'decreased endocochlear potential')]
MGI:3649489 Gm12247 []
MGI:1354958 Hs6st1 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('GO:0015012PHENOTYPE', None), ('MP:0005104', 'abnormal tarsal bone morphology'), ('MP:0003231', 'abnormal placenta vasculature'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('HP:0004325', None), ('HP:0012372', None), ('MP:0011099', 'lethality throughout fetal growth and development, complete penetrance'), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('MP:0001698', 'decreased embryo size'), ('GO:0048286PHENOTYPE', None), ('MP:0001183', 'overexpanded pulmonary alveoli')]
MGI:108057 Rpl6 []
MGI:3643356 Rpsa-ps2 []
MGI:2142985 Xylb [('MP:0005316', 'abnormal response to tactile stimuli'), ('MP:0003960', 'increased lean body mass'), ('HP:0011001', None), ('MP:0001486', 'abnormal startle reflex'), ('HP:0010683', None)]
MGI:2685590 Rubcnl []
MGI:2442443 Fsd1l []
MGI:2441932 Prepl [('HP:0001252', None), ('MP:0001523', 'impaired righting response'), ('MP:0001258', 'decreased body length')]
MGI:3705885 Rpl10a-ps1 []
MGI:95525 Fgfr4 [('HP:0004325', None)]
MGI:5454996 Gm25219 []
MGI:97797 Ptgs1 [('MP:0004841', 'abnormal small intestine crypts of Lieberkuhn morphology'), ('MP:0012124', 'increased bronchoconstrictive response'), ('MP:0008800', 'increased small intestinal crypt cell apoptosis'), ('MP:0005389', 'reproductive system phenotype'), ('HP:0002664', None), ('MP:0005531', 'increased renal vascular resistance'), ('MP:0005527', 'increased renal glomerular filtration rate'), ('GO:0008217PHENOTYPE', None), ('HP:0001744', None), ('MP:0005186', 'increased circulating progesterone level'), ('MP:0009815', 'decreased prostaglandin level'), ('GO:0006693PHENOTYPE', None), ('MP:0003436', 'decreased susceptibility to induced arthritis'), ('MP:0009766', 'increased sensitivity to xenobiotic induced morbidity/mortality'), ('MP:0005378', 'growth/size/body region phenotype'), ('HP:0012533', None), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0000233', 'abnormal blood flow velocity'), ('MP:0009818', 'abnormal thromboxane level'), ('MP:0009814', 'increased prostaglandin level'), ('MP:0011021', 'abnormal circadian regulation of heart rate'), ('MP:0003631', 'nervous system phenotype'), ('HP:0003110', None), ('HP:0011869', None), ('MP:0002736', 'abnormal nociception after inflammation'), ('MP:0003718', 'maternal effect'), ('MP:0002551', 'abnormal blood coagulation')]
MGI:2442631 Mob1a []
MGI:109246 Htr4 [('MP:0005449', 'abnormal food intake')]
MGI:2139793 Cept1 []
MGI:3650528 Gm13017 []
MGI:2142885 Cog8 []
MGI:5452840 Gm23063 []
MGI:3783117 Gm15675 []
MGI:5454950 Gm25173 []
MGI:1860489 Ptbp2 [('HP:0001508', None), ('MP:0002199', 'abnormal brain commissure morphology'), ('MP:0008128', 'abnormal brain internal capsule morphology'), ('HP:0002180', None), ('MP:0008489', 'slow postnatal weight gain'), ('HP:0003811', None), ('MP:0008283', 'small hippocampus'), ('MP:0008458', 'abnormal cortical ventricular zone morphology'), ('MP:0001265', 'decreased body size'), ('MP:0008026', 'abnormal brain white matter morphology'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('MP:0004981', 'decreased neuronal precursor cell number')]
MGI:1333759 Vmn1r51 []
MGI:2138271 Gorab [('MP:0000379', 'decreased hair follicle number'), ('MP:0001243', 'abnormal dermal layer morphology'), ('MP:0009545', 'abnormal dermis papillary layer morphology'), ('HP:0002098', None), ('MP:0001786', 'skin edema')]
MGI:3588216 Ube2u []
MGI:4414979 Gm16559 []
MGI:2443194 Harbi1 []
MGI:1923822 Rbmxl2 []
MGI:87886 Chrna2 [('MP:0002945', 'abnormal inhibitory postsynaptic currents'), ('MP:0002910', 'abnormal excitatory postsynaptic currents'), ('MP:0003631', 'nervous system phenotype')]
MGI:5000466 Anpep [('MP:0003711', 'pathological neovascularization'), ('MP:0002451', 'abnormal macrophage physiology'), ('MP:0005387', 'immune system phenotype'), ('MP:0012075', 'impaired mammary gland growth during pregnancy')]
MGI:1914588 Rffl []
MGI:109501 Crat [('HP:0000816', None), ('MP:0005559', 'increased circulating glucose level'), ('MP:0002702', 'decreased circulating free fatty acid level'), ('MP:0004215', 'abnormal myocardial fiber physiology')]
MGI:3645406 Gm8430 []
MGI:3645521 Gm4735 []
MGI:3583959 Fam188b []
MGI:5477043 Gm26549 []
MGI:1913839 Ube2v1 []
MGI:5456093 Gm26316 []
MGI:3650465 Gm13182 []
MGI:3649532 Gm13675 []
MGI:2147707 Kank1 []
MGI:1202069 Cdk2ap1 [('GO:0001701PHENOTYPE', None), ('GO:0060325PHENOTYPE', None)]
MGI:1925558 Gpr108 []
MGI:5455394 Gm25617 []
MGI:3642592 Gm10603 []
MGI:3644647 Gm6071 []
MGI:98224 Saa4 []
MGI:1924753 Alg9 []
MGI:3651632 Gm11222 []
MGI:3826530 Gm16316 []
MGI:1913417 Tma7 []
MGI:2442862 Fcrl1 []
MGI:1918619 Krtap16-3 []
MGI:2676794 Mirlet7b []
MGI:3704330 Gm10177 []
MGI:2444308 Atg4d [('HP:0007703', None)]
MGI:3651276 Gm11628 []
MGI:96428 Ifi203 []
MGI:1349215 Abcd1 [('MP:0008295', 'abnormal adrenal gland zona reticularis morphology'), ('MP:0000136', 'abnormal microglial cell morphology'), ('MP:0003313', 'abnormal locomotor activation'), ('HP:0002143', None), ('MP:0008294', 'abnormal adrenal gland zona fasciculata morphology'), ('MP:0001921', 'reduced fertility'), ('MP:0008288', 'abnormal adrenal cortex morphology'), ('HP:0002446', None), ('MP:0005284', 'increased saturated fatty acid level'), ('MP:0005281', 'increased fatty acid level'), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:1923558 Rab1b [('MP:0002169', 'no abnormal phenotype detected')]
MGI:1341879 Zim1 []
MGI:5453132 Gm23355 []
MGI:2685446 Nuggc [('MP:0005387', 'immune system phenotype'), ('GO:0016446PHENOTYPE', None)]
MGI:3650968 Gm12630 []
MGI:1927667 Defb4 []
MGI:96930 Mcc [('MP:0002169', 'no abnormal phenotype detected')]
MGI:1289308 Tax1bp1 [('MP:0008687', 'increased interleukin-2 secretion'), ('MP:0008657', 'increased interleukin-1 beta secretion'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0002442', 'abnormal leukocyte physiology'), ('HP:0011017', None), ('HP:0001627', None), ('MP:0001853', 'heart inflammation'), ('MP:0008489', 'slow postnatal weight gain'), ('MP:0000285', 'abnormal heart valve morphology'), ('MP:0008560', 'increased tumor necrosis factor secretion'), ('MP:0002148', 'abnormal hypersensitivity reaction')]
MGI:109240 Ube2l3 [('MP:0011089', 'perinatal lethality, complete penetrance'), ('MP:0003984', 'embryonic growth retardation'), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('MP:0001711', 'abnormal placenta morphology')]
MGI:2441809 A530099J19Rik []
MGI:1933114 Bcl9l []
MGI:1929237 Pus1 [('MP:0004819', 'decreased skeletal muscle mass'), ('HP:0011804', None)]
MGI:1914498 Rpl39 []
MGI:2144158 Nudcd3 []
MGI:3651491 Gm13864 []
MGI:3705508 Gm14648 []
MGI:109544 Aim1 [('MP:0009142', 'decreased prepulse inhibition'), ('MP:0013293', 'embryonic lethality prior to tooth bud stage'), ('MP:0013292', 'embryonic lethality prior to organogenesis'), ('MP:0001402', 'hypoactivity'), ('MP:0003232', 'abnormal forebrain development'), ('HP:0045005', None), ('HP:0002143', None), ('MP:0006108', 'abnormal hindbrain development')]
MGI:5313104 Gm20657 []
MGI:5452900 Gm23123 []
MGI:1353636 Abt1 []
MGI:5477078 Gm26584 []
MGI:4421975 n-R5s123 []
MGI:3704189 Rpl31-ps14 []
MGI:2387430 Trim60 []
MGI:2138584 Gigyf2 [('GO:0050881PHENOTYPE', None), ('GO:0008344PHENOTYPE', None), ('HP:0004325', None), ('MP:0001258', 'decreased body length'), ('MP:0008946', 'abnormal neuron number'), ('GO:0044267PHENOTYPE', None), ('GO:0035264PHENOTYPE', None), ('HP:0002180', None), ('MP:0005449', 'abnormal food intake'), ('GO:0021522PHENOTYPE', None), ('MP:0001405', 'impaired coordination'), ('MP:0002066', 'abnormal motor capabilities/coordination/movement'), ('MP:0008493', 'alpha-synuclein inclusion body'), ('GO:0009791PHENOTYPE', None)]
MGI:2388477 Igsf11 []
MGI:104803 Siae [('MP:0008499', 'increased IgG1 level'), ('HP:0003496', None), ('HP:0006270', None), ('MP:0008501', 'increased IgG2b level'), ('HP:0005403', None), ('MP:0008502', 'increased IgG3 level'), ('MP:0008182', 'decreased marginal zone B cell number'), ('MP:0005154', 'increased B cell proliferation'), ('MP:0004771', 'increased anti-single stranded DNA antibody level'), ('MP:0008500', 'increased IgG2a level'), ('HP:0003212', None), ('MP:0008169', 'increased B-1b cell number'), ('MP:0004762', 'increased anti-double stranded DNA antibody level')]
MGI:105932 Inhbc [('MP:0005370', 'liver/biliary system phenotype'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0005389', 'reproductive system phenotype')]
MGI:1925678 Pym1 []
MGI:2142489 Ido2 [('MP:0005616', 'decreased susceptibility to type IV hypersensitivity reaction'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0004946', 'abnormal regulatory T cell physiology'), ('MP:0008706', 'decreased interleukin-6 secretion'), ('MP:0008561', 'decreased tumor necrosis factor secretion'), ('HP:0002664', None)]
MGI:1914346 Mmachc [('MP:0005382', 'craniofacial phenotype'), ('MP:0002989', 'small kidney'), ('HP:0012372', None), ('HP:0000035', None), ('MP:0001697', 'abnormal embryo size'), ('MP:0004002', 'abnormal jejunum morphology'), ('MP:0000702', 'enlarged lymph nodes'), ('MP:0009931', 'abnormal skin appearance'), ('HP:0001789', None), ('HP:0010683', None)]
MGI:1924356 Tmem181a []
MGI:3705279 Gm14508 []
MGI:1916510 Ing2 [('MP:0000693', 'spleen hyperplasia'), ('GO:2001234PHENOTYPE', None), ('MP:0004901', 'decreased male germ cell number'), ('MP:0001154', 'seminiferous tubule degeneration'), ('HP:0012205', None), ('MP:0009321', 'increased histiocytic sarcoma incidence'), ('MP:0009839', 'multiflagellated sperm'), ('MP:0009238', 'coiled sperm flagellum'), ('MP:0010769', 'abnormal survival'), ('HP:0012864', None), ('MP:0006042', 'increased apoptosis'), ('MP:0009239', 'short sperm flagellum'), ('MP:0013538', 'increased Harderian gland adenoma incidence')]
MGI:3648357 Gm7393 []
MGI:3779224 Gm11008 []
MGI:1921519 1700063H04Rik []
MGI:104717 Meis1 [('GO:0060216PHENOTYPE', None), ('HP:0008063', None), ('MP:0008253', 'absent megakaryocytes'), ('HP:0000980', None), ('MP:0020327', 'abnormal capillary branching pattern'), ('GO:0030097PHENOTYPE', None), ('HP:0001627', None), ('MP:0004809', 'increased hematopoietic stem cell number'), ('MP:0010763', 'abnormal hematopoietic stem cell physiology'), ('HP:0000568', None), ('HP:0025016', None), ('MP:0003227', 'abnormal vascular branching morphogenesis'), ('MP:0003714', 'absent platelets'), ('GO:0007626PHENOTYPE', None), ('MP:0002135', 'abnormal kidney morphology'), ('HP:0002597', None), ('MP:0002398', 'abnormal bone marrow cell morphology/development'), ('HP:0001629', None), ('GO:0048514PHENOTYPE', None)]
MGI:1354698 Fbxw4 []
MGI:1352464 Nr1h4 [('MP:0009305', 'decreased retroperitoneal fat pad weight'), ('MP:0003324', 'increased liver adenoma incidence'), ('MP:0009342', 'enlarged gallbladder'), ('MP:0011939', 'increased food intake'), ('HP:0001402', None), ('MP:0009289', 'decreased epididymal fat pad weight'), ('MP:0002944', 'increased lactate dehydrogenase level'), ('MP:0009642', 'abnormal blood homeostasis'), ('HP:0003073', None), ('HP:0040216', None), ('MP:0004774', 'abnormal bile salt level'), ('MP:0008988', 'abnormal liver perisinusoidal space morphology'), ('MP:0002981', 'increased liver weight'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0010124', 'decreased bone mineral content'), ('HP:0001392', None), ('MP:0009133', 'decreased white fat cell size'), ('MP:0008019', 'increased liver tumor incidence'), ('HP:0002910', None), ('HP:0005237', None), ('MP:0002941', 'increased circulating alanine transaminase level'), ('HP:0002240', None), ('MP:0005365', 'abnormal bile salt homeostasis'), ('HP:0004326', None), ('HP:0008887', None), ('MP:0008989', 'abnormal liver sinusoid morphology'), ('MP:0010182', 'decreased susceptibility to weight gain'), ('MP:0011941', 'increased fluid intake'), ('HP:0003124', None), ('MP:0010771', 'integument phenotype')]
MGI:1919338 Ush1c [('MP:0001394', 'circling'), ('MP:0006358', 'absent pinna reflex'), ('HP:0003228', None), ('MP:0010053', 'decreased grip strength'), ('MP:0004522', 'abnormal orientation of cochlear hair cell stereociliary bundles'), ('MP:0004399', 'abnormal cochlear outer hair cell morphology'), ('MP:0008805', 'decreased circulating amylase level'), ('GO:0042491PHENOTYPE', None), ('MP:0004491', 'abnormal orientation of outer hair cell stereociliary bundles'), ('HP:0040216', None), ('MP:0010123', 'increased bone mineral content'), ('MP:0001410', 'head bobbing'), ('HP:0003233', None), ('MP:0001525', 'impaired balance'), ('MP:0004577', 'abnormal cochlear hair cell inter-stereocilial links morphology'), ('MP:0008141', 'decreased small intestinal microvillus size'), ('MP:0004431', 'abnormal hair cell mechanoelectric transduction'), ('MP:0003960', 'increased lean body mass'), ('HP:0008887', None), ('MP:0000043', 'organ of Corti degeneration'), ('HP:0011877', None), ('HP:0000733', None), ('MP:0000495', 'abnormal colon morphology'), ('HP:0000546', None)]
MGI:3651359 Gm12508 []
MGI:1916255 Ubald1 []
MGI:2444853 Ttc26 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0008892', 'abnormal sperm flagellum morphology'), ('MP:0005169', 'abnormal male meiosis'), ('MP:0002948', 'abnormal neuron specification'), ('MP:0003729', 'abnormal photoreceptor outer segment morphology'), ('MP:0002151', 'abnormal neural tube morphology'), ('MP:0002784', 'abnormal Sertoli cell morphology'), ('HP:0010442', None), ('MP:0004131', 'abnormal motile primary cilium morphology'), ('MP:0002084', 'abnormal developmental patterning'), ('MP:0004289', 'abnormal bony labyrinth'), ('HP:0000238', None)]
MGI:1923434 Abca6 [('HP:0003075', None)]
MGI:1913904 Tex12 [('HP:0000134', None), ('MP:0005168', 'abnormal female meiosis'), ('HP:0008222', None)]
MGI:1916796 Mapk1ip1 []
MGI:4421990 n-R5s134 []
MGI:3650419 Snrpert []
MGI:1913608 Adprm []
MGI:2443738 BC022687 []
MGI:3643406 Rps3a3 []
MGI:1921076 Poldip3 []
MGI:107995 Upf1 [('MP:0001672', 'abnormal embryo development')]
MGI:3650839 Gm12168 []
MGI:1914120 Serbp1 []
MGI:2141314 Naa11 []
MGI:1922238 4930480G23Rik []
MGI:4439773 Igkv12-46 []
MGI:3801742 Gm14925 []
MGI:3576049 Ugt1a2 []
MGI:3649338 Gm12219 []
MGI:1915094 Rab32 [('MP:0011110', 'preweaning lethality, incomplete penetrance')]
MGI:2683087 Zhx2 [('HP:0010876', None)]
MGI:4421986 n-R5s130 []
MGI:2142048 Rnf40 []
MGI:3649231 Gm13199 []
MGI:3802009 Gm16165 []
MGI:5456101 Gm26324 []
MGI:1924029 Cluap1 [('MP:0011998', 'decreased embryonic cilium length'), ('MP:0000929', 'open neural tube'), ('MP:0003400', 'kinked neural tube'), ('GO:0060972PHENOTYPE', None), ('GO:0021508PHENOTYPE', None), ('MP:0011099', 'lethality throughout fetal growth and development, complete penetrance'), ('MP:0000291', 'enlarged pericardium'), ('MP:0001700', 'abnormal embryo turning'), ('MP:0001265', 'decreased body size'), ('GO:0035082PHENOTYPE', None), ('GO:0007224PHENOTYPE', None), ('MP:0000358', 'abnormal cell morphology'), ('MP:0010117', 'abnormal lateral plate mesoderm morphology'), ('MP:0004132', 'absent embryonic cilia')]
MGI:1891731 Stub1 [('MP:0009541', 'increased thymocyte apoptosis'), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0004250', 'tau protein deposits'), ('HP:0004325', None), ('MP:0001407', 'short stride length'), ('MP:0001489', 'decreased startle reflex'), ('MP:0005165', 'increased susceptibility to injury'), ('MP:0012307', 'impaired spatial learning'), ('MP:0001921', 'reduced fertility'), ('MP:0005130', 'decreased follicle stimulating hormone level'), ('HP:0000778', None), ('MP:0001648', 'abnormal apoptosis'), ('HP:0011017', None), ('MP:0004852', 'decreased testis weight')]
MGI:3782724 Gm4540 []
MGI:2685586 Cyb5d1 [('HP:0003228', None), ('MP:0005561', 'increased mean corpuscular hemoglobin'), ('HP:0008887', None)]
MGI:2444772 Zhx3 [('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('MP:0010053', 'decreased grip strength'), ('HP:0010504', None), ('HP:0011001', None)]
MGI:109561 Sp100 []
MGI:5454663 Gm24886 []
MGI:97371 Npr1 [('MP:0002083', 'premature death'), ('MP:0000278', 'abnormal myocardial fiber morphology'), ('HP:0030875', None), ('HP:0040171', None), ('MP:0001273', 'decreased metastatic potential'), ('MP:0006144', 'increased systemic arterial systolic blood pressure'), ('HP:0000822', None), ('MP:0006143', 'increased systemic arterial diastolic blood pressure'), ('MP:0002842', 'increased systemic arterial blood pressure'), ('MP:0005647', 'abnormal sex gland physiology'), ('MP:0000230', 'abnormal systemic arterial blood pressure'), ('MP:0001625', 'cardiac hypertrophy'), ('MP:0001613', 'abnormal vasodilation'), ('MP:0001272', 'increased metastatic potential'), ('HP:0001640', None), ('HP:0002647', None), ('MP:0004875', 'increased mean systemic arterial blood pressure'), ('HP:0001635', None), ('MP:0005329', 'abnormal myocardium layer morphology'), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality'), ('MP:0002843', 'decreased systemic arterial blood pressure')]
MGI:3641755 Gm10804 []
MGI:5455565 Gm25788 []
MGI:2386964 St7l []
MGI:4421933 n-R5s85 []
MGI:1336212 Ncr1 [('MP:0005397', 'hematopoietic system phenotype'), ('HP:0012177', None), ('HP:0005415', None), ('GO:0051607PHENOTYPE', None), ('HP:0040218', None), ('MP:0009790', 'decreased susceptibility to viral infection induced morbidity/mortality'), ('MP:0005070', 'impaired natural killer cell mediated cytotoxicity')]
MGI:1921898 S100pbp []
MGI:3651313 Gm11824 []
MGI:5531091 Mir6986 []
MGI:1915207 March5 []
MGI:5452882 Gm23105 []
MGI:5313124 Gm20677 []
MGI:1097706 Cetn3 []
MGI:1929008 Copz2 []
MGI:3650896 Rpl26-ps5 []
MGI:1298386 Tpd52l1 []
MGI:1914199 Trim59 []
MGI:3648831 Rpl31-ps16 []
MGI:102484 mt-Ti []
MGI:3646682 Rpl13-ps3 []
MGI:3650227 Gm13340 []
MGI:1919027 Ing3 []
MGI:894668 Serpinb9b []
MGI:98815 Crisp2 []
MGI:1915433 Bcas2 []
MGI:2443764 Iqcb1 []
MGI:2140175 Ldlrap1 [('HP:0002155', None), ('MP:0000181', 'abnormal circulating LDL cholesterol level'), ('GO:0042632PHENOTYPE', None), ('HP:0003124', None)]
MGI:1926341 Sult1d1 []
MGI:98287 Srsf5 []
MGI:3652015 Gm11410 []
MGI:3782979 Gm15531 []
MGI:3641908 Gm10073 []
MGI:1915500 Fam96a [('MP:0003047', 'abnormal thoracic vertebrae morphology'), ('MP:0010851', 'decreased effector memory CD8-positive, alpha-beta T cell number'), ('HP:0010683', None)]
MGI:95420 Ces1c [('HP:0002045', None)]
MGI:3643291 Gm5436 []
MGI:95402 Epb42 [('HP:0001944', None), ('GO:0048536PHENOTYPE', None), ('MP:0002874', 'decreased hemoglobin content'), ('HP:0004444', None), ('MP:0008809', 'increased spleen iron level'), ('MP:0000208', 'decreased hematocrit'), ('HP:0001923', None)]
MGI:3648523 Gm5453 []
MGI:3650664 Gm13050 []
MGI:5452063 Gm22286 []
MGI:1929878 Smoc1 [('HP:0001440', None), ('HP:0000609', None), ('HP:0000708', None), ('MP:0000455', 'abnormal maxilla morphology'), ('MP:0011085', 'postnatal lethality, complete penetrance'), ('HP:0002990', None), ('HP:0001508', None), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('MP:0000571', 'interdigital webbing'), ('HP:0005922', None), ('HP:0000175', None), ('MP:0009874', 'abnormal interdigital cell death'), ('HP:0000921', None), ('MP:0005201', 'abnormal retinal pigment epithelium morphology'), ('MP:0000556', 'abnormal hindlimb morphology'), ('HP:0012521', None), ('HP:0000546', None), ('HP:0011499', None), ('HP:0002982', None), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('HP:0007973', None)]
MGI:106362 Sco1 [('MP:0010244', 'decreased kidney copper level'), ('HP:0001392', None), ('HP:0040014', None), ('HP:0001824', None), ('MP:0001265', 'decreased body size'), ('MP:0003067', 'decreased liver copper level'), ('GO:0006878PHENOTYPE', None)]
MGI:3643695 Gm8055 []
MGI:2141142 Rpap2 []
MGI:2447166 Cmtm7 []
MGI:3650588 Gm12416 []
MGI:90168 Dcaf11 [('MP:0004647', 'decreased lumbar vertebrae number'), ('HP:0005518', None)]
MGI:3643810 Zscan18 []
MGI:5456192 Gm26415 []
MGI:1919057 Tars2 []
MGI:1354184 Nox4 [('HP:0002088', None), ('MP:0003223', 'decreased cardiomyocyte apoptosis'), ('HP:0001712', None), ('MP:0006036', 'abnormal mitochondrial physiology'), ('MP:0003141', 'cardiac fibrosis'), ('MP:0004485', 'increased response of heart to induced stress'), ('MP:0003204', 'decreased neuron apoptosis'), ('MP:0010181', 'decreased susceptibility to weight loss'), ('MP:0001625', 'cardiac hypertrophy'), ('MP:0002833', 'increased heart weight'), ('MP:0005598', 'decreased ventricle muscle contractility'), ('MP:0004937', 'dilated heart'), ('MP:0003674', 'oxidative stress'), ('MP:0010724', 'thick interventricular septum'), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality')]
MGI:98662 Tec []
MGI:3648477 Gm14928 []
MGI:3704451 Gm10076 []
MGI:5530708 Gm27326 []
MGI:2444070 Nlrc3 [('MP:0008560', 'increased tumor necrosis factor secretion'), ('MP:0008735', 'increased susceptibility to endotoxin shock'), ('MP:0008553', 'increased circulating tumor necrosis factor level')]
MGI:3651796 Rps11-ps2 []
MGI:3643597 Gm8213 []
MGI:5455373 Gm25596 []
MGI:3641693 Gm10335 []
MGI:109505 Xlr3b []
MGI:1923665 Fam187b []
MGI:3650221 Gm13339 []
MGI:1916949 2310079G19Rik []
MGI:3646811 Gm8624 []
MGI:5439751 Gm21994 []
MGI:2148180 Snora69 []
MGI:5477162 Gm26668 []
MGI:97286 Ncl []
MGI:109173 Dsc1 [('HP:0007957', None), ('HP:0004325', None), ('MP:0001194', 'dermatitis'), ('MP:0001195', 'flaky skin'), ('MP:0001236', 'abnormal epidermis stratum spinosum morphology'), ('HP:0001036', None), ('HP:0000962', None), ('HP:0100792', None), ('MP:0001511', 'disheveled coat'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0002656', 'abnormal keratinocyte differentiation')]
MGI:5452841 Gm23064 []
MGI:5453907 Gm24130 []
MGI:5453073 Gm23296 []
MGI:1310002 Fmo1 []
MGI:3651145 Gm12936 []
MGI:1923786 Mmadhc []
MGI:3705734 Gm14681 []
MGI:2148251 Ddx19b []
MGI:97279 Nat1 [('MP:0008875', 'abnormal xenobiotic pharmacokinetics'), ('MP:0001921', 'reduced fertility')]
MGI:3039592 Fads6 []
MGI:3781533 Gm3355 []
MGI:4360056 Snora78 []
MGI:3643039 Gm4852 []
MGI:1921729 Snx11 []
MGI:1918648 5430427O19Rik []
MGI:1929745 Cdc42ep5 []
MGI:3704272 Gm10443 []
MGI:1914312 Slc25a53 []
MGI:1920511 1700037C18Rik []
MGI:3643536 Gm8508 []
MGI:101813 Coq3 []
MGI:2153045 Elmo2 []
MGI:1918039 Kynu []
MGI:3650926 Gm14388 []
MGI:5453975 Gm24198 []
MGI:3649569 Gm12781 []
MGI:1201406 Slc10a2 [('HP:0002910', None), ('MP:0004773', 'abnormal bile composition')]
MGI:2444989 Spg11 [('MP:0009940', 'abnormal hippocampus pyramidal cell morphology'), ('HP:0002062', None), ('MP:0000876', 'Purkinje cell degeneration'), ('HP:0012757', None), ('MP:0003224', 'neuron degeneration'), ('MP:0008260', 'abnormal autophagy'), ('MP:0005058', 'abnormal lysosome morphology'), ('MP:0001405', 'impaired coordination'), ('HP:0001824', None), ('GO:0007040PHENOTYPE', None)]
MGI:1923511 0610040J01Rik []
MGI:96213 Hpd [('HP:0003110', None)]
MGI:3641865 Gm10357 []
MGI:88192 Smarca4 [('MP:0006126', 'abnormal cardiac outflow tract development'), ('MP:0000740', 'impaired smooth muscle contractility'), ('HP:0030875', None), ('MP:0009476', 'enlarged cecum'), ('GO:0061626PHENOTYPE', None), ('HP:0006517', None), ('MP:0010656', 'thick myocardium'), ('HP:0000347', None), ('MP:0010819', 'primary atelectasis'), ('HP:0000958', None), ('MP:0005092', 'decreased double-positive T cell number'), ('HP:0002669', None), ('HP:0001629', None), ('MP:0002747', 'abnormal aortic valve morphology'), ('MP:0011684', 'coronary-cameral fistula to right ventricle'), ('MP:0000297', 'abnormal atrioventricular cushion morphology'), ('HP:0002623', None), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('HP:0003826', None), ('MP:0002746', 'abnormal semilunar valve morphology'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance'), ('MP:0010602', 'abnormal pulmonary valve cusp morphology'), ('GO:0048562PHENOTYPE', None), ('HP:0000778', None), ('HP:0011804', None), ('HP:0001640', None), ('GO:0043966PHENOTYPE', None), ('GO:0060318PHENOTYPE', None), ('MP:0005294', 'abnormal heart ventricle morphology'), ('MP:0000727', 'absent CD8-positive, alpha-beta T cells'), ('HP:0001877', None), ('MP:0002796', 'impaired skin barrier function'), ('MP:0002145', 'abnormal T cell differentiation'), ('MP:0000715', 'decreased thymocyte number'), ('MP:0005384', 'cellular phenotype'), ('HP:0001647', None), ('MP:0010595', 'abnormal aortic valve cusp morphology'), ('MP:0008802', 'abnormal intestinal smooth muscle morphology'), ('MP:0008083', 'decreased single-positive T cell number'), ('MP:0004937', 'dilated heart'), ('GO:0001701PHENOTYPE', None), ('GO:0001832PHENOTYPE', None), ('HP:0001635', None), ('MP:0003857', 'abnormal hindlimb zeugopod morphology'), ('MP:0000556', 'abnormal hindlimb morphology'), ('HP:0000528', None), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0010412', 'atrioventricular septal defect'), ('MP:0001196', 'shiny skin'), ('MP:0002731', 'megacolon'), ('GO:0003151PHENOTYPE', None), ('HP:0011121', None), ('MP:0011648', 'thick heart valve cusps'), ('MP:0001883', 'increased mammary adenocarcinoma incidence'), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('HP:0011297', None), ('GO:0043066PHENOTYPE', None), ('MP:0008770', 'decreased survivor rate'), ('MP:0002020', 'increased tumor incidence')]
MGI:3651089 Gm13171 []
MGI:3802104 Gm16238 []
MGI:2443361 6430550D23Rik []
MGI:3707466 Gm15369 []
MGI:102500 mt-Nd2 []
MGI:5452861 Gm23084 []
MGI:1918185 Speer4f1 []
MGI:5012000 Gm19815 []
MGI:3704312 Gm10282 []
MGI:2652836 Gmeb2 []
MGI:3796835 Gm10509 []
MGI:3704203 Gm10171 []
MGI:109351 Slc4a2 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0001541', 'abnormal osteoclast physiology'), ('MP:0002083', 'premature death'), ('HP:0001508', None), ('HP:0003251', None), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0005605', 'increased bone mass'), ('MP:0010878', 'increased trabecular bone volume'), ('MP:0000468', 'abnormal esophageal epithelium morphology'), ('MP:0010868', 'increased bone trabecula number'), ('MP:0013566', 'dilated gastric glands'), ('MP:0001438', 'aphagia'), ('MP:0002661', 'abnormal corpus epididymis morphology'), ('MP:0002662', 'abnormal cauda epididymis morphology'), ('MP:0000133', 'abnormal long bone metaphysis morphology'), ('MP:0009703', 'decreased birth body size'), ('HP:0000365', None), ('MP:0009347', 'increased trabecular bone thickness'), ('HP:0011002', None), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:3652225 Hnf1aos1 []
MGI:1889842 Fam13a []
MGI:2151045 Lsm10 []
MGI:98430 Sult2a1 []
MGI:4422060 n-R5s195 []
MGI:1924963 Peli3 [('MP:0009790', 'decreased susceptibility to viral infection induced morbidity/mortality'), ('HP:0011017', None), ('MP:0002410', 'decreased susceptibility to viral infection'), ('MP:0011072', 'abnormal macrophage cytokine production')]
MGI:2681861 Proser3 []
MGI:3647156 Gm6682 []
MGI:2685270 Pm20d2 []
MGI:1913869 Atat1 [('MP:0008280', 'abnormal male germ cell apoptosis'), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0001922', 'reduced male fertility'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0002675', 'asthenozoospermia'), ('HP:0012864', None), ('GO:0007283PHENOTYPE', None), ('MP:0004929', 'decreased epididymis weight'), ('MP:0009239', 'short sperm flagellum'), ('HP:0040006', None), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0004852', 'decreased testis weight')]
MGI:1925127 6030458C11Rik []
MGI:2444401 Snrnp200 [('MP:0011100', 'preweaning lethality, complete penetrance'), ('HP:0012165', None), ('MP:0013293', 'embryonic lethality prior to tooth bud stage'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance')]
MGI:96161 Hmga1-rs1 []
MGI:97172 Mt2 [('GO:0006882PHENOTYPE', None)]
MGI:3649743 Gm12229 []
MGI:1916560 Pacrg []
MGI:2676856 Mir192 [('MP:0006315', 'abnormal urine protein level'), ('MP:0002135', 'abnormal kidney morphology'), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic'), ('MP:0011338', 'abnormal mesangial matrix morphology')]
MGI:3781339 Gm3160 []
MGI:102480 mt-Tm []
MGI:3629654 Mir692-2 []
MGI:1923052 4930458D05Rik []
MGI:3782854 Gm4673 []
MGI:1916428 Snx5 [('MP:0005631', 'decreased lung weight'), ('HP:0001508', None), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('HP:0004325', None), ('MP:0002942', 'decreased circulating alanine transaminase level'), ('HP:0003468', None), ('MP:0000183', 'decreased circulating LDL cholesterol level'), ('HP:0009887', None), ('MP:0001265', 'decreased body size'), ('HP:0000961', None), ('HP:0003233', None), ('HP:0003146', None)]
MGI:4421994 n-R5s138 []
MGI:1915562 Gstm7 []
MGI:1289301 Ubxn1 []
MGI:1913664 Ndufa12 []
MGI:1920708 Aldh3b3 []
MGI:3647161 Mup-ps6 []
MGI:3641838 Gm10247 []
MGI:1196620 Hpn [('HP:0010679', None), ('HP:0012153', None), ('MP:0002855', 'abnormal cochlear ganglion morphology'), ('GO:0007605PHENOTYPE', None), ('MP:0003149', 'abnormal tectorial membrane morphology'), ('MP:0005471', 'decreased thyroxine level'), ('MP:0011967', 'increased or absent threshold for auditory brainstem response'), ('MP:0004434', 'abnormal cochlear outer hair cell physiology'), ('HP:0000365', None), ('HP:0000821', None), ('MP:0001158', 'abnormal prostate gland morphology')]
MGI:2144724 Ttc7b []
MGI:3705630 Gm15190 []
MGI:3045379 4732419C18Rik []
MGI:3781169 Gm2991 []
MGI:1916856 Mtfmt [('MP:0011100', 'preweaning lethality, complete penetrance')]
MGI:88373 Cebpb [('MP:0003355', 'decreased ovulation rate'), ('MP:0002083', 'premature death'), ('MP:0008472', 'abnormal spleen secondary B follicle morphology'), ('MP:0002357', 'abnormal spleen white pulp morphology'), ('HP:0003237', None), ('MP:0005666', 'abnormal adipose tissue physiology'), ('MP:0008395', 'abnormal osteoblast differentiation'), ('HP:0000962', None), ('MP:0004130', 'abnormal muscle cell glucose uptake'), ('MP:0010172', 'abnormal mammary gland epithelium physiology'), ('MP:0000702', 'enlarged lymph nodes'), ('MP:0001129', 'impaired ovarian folliculogenesis'), ('MP:0000628', 'abnormal mammary gland development'), ('MP:0002500', 'granulomatous inflammation'), ('MP:0009114', 'decreased pancreatic beta cell mass'), ('MP:0002451', 'abnormal macrophage physiology'), ('HP:0011842', None), ('MP:0001222', 'epidermal hyperplasia'), ('MP:0008944', 'decreased sensitivity to induced cell death'), ('MP:0002702', 'decreased circulating free fatty acid level'), ('MP:0010868', 'increased bone trabecula number'), ('MP:0013239', 'impaired skeletal muscle regeneration'), ('MP:0002344', 'abnormal lymph node B cell domain morphology'), ('GO:0060644PHENOTYPE', None), ('MP:0003058', 'increased insulin secretion'), ('MP:0002343', 'abnormal lymph node cortex morphology'), ('HP:0100249', None), ('MP:0002971', 'abnormal brown adipose tissue morphology'), ('MP:0005639', 'hemosiderosis'), ('MP:0005399', 'increased susceptibility to fungal infection'), ('MP:0004502', 'decreased incidence of tumors by chemical induction'), ('HP:0040216', None), ('MP:0005466', 'abnormal T-helper 2 physiology'), ('MP:0002742', 'enlarged submandibular lymph nodes'), ('MP:0004985', 'decreased osteoclast cell number'), ('MP:0011427', 'mesangial cell hyperplasia'), ('MP:0003383', 'abnormal gluconeogenesis'), ('MP:0000321', 'increased bone marrow cell number'), ('MP:0009419', 'skeletal muscle fibrosis'), ('MP:0008734', 'decreased susceptibility to endotoxin shock'), ('MP:0009307', 'decreased uterine fat pad weight'), ('MP:0005006', 'abnormal osteoblast physiology'), ('MP:0010876', 'decreased bone volume'), ('HP:0001967', None), ('MP:0008596', 'increased circulating interleukin-6 level'), ('MP:0005319', 'abnormal enzyme/coenzyme level'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0008663', 'increased interleukin-12 secretion'), ('MP:0008873', 'increased physiological sensitivity to xenobiotic'), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0002421', 'abnormal cell-mediated immunity'), ('MP:0004817', 'abnormal skeletal muscle mass'), ('MP:0002347', 'abnormal lymph node T cell domain morphology'), ('MP:0008033', 'impaired lipolysis'), ('MP:0020080', 'increased bone mineralization'), ('HP:0001547', None), ('MP:0011224', 'abnormal lymph node medullary cord morphology'), ('HP:0002718', None), ('MP:0002346', 'abnormal lymph node secondary follicle morphology'), ('MP:0005517', 'decreased liver regeneration'), ('MP:0009347', 'increased trabecular bone thickness'), ('HP:0001978', None), ('MP:0001882', 'abnormal lactation'), ('MP:0000352', 'decreased cell proliferation'), ('MP:0008618', 'decreased circulating interleukin-12 level')]
MGI:5454350 Gm24573 []
MGI:3036289 Cd200r4 []
MGI:3652158 Gm13416 []
MGI:1277962 Hus1 [('MP:0011204', 'abnormal visceral yolk sac blood island morphology'), ('MP:0003229', 'abnormal vitelline vasculature morphology'), ('GO:0006468PHENOTYPE', None), ('MP:0003984', 'embryonic growth retardation'), ('MP:0004030', 'induced chromosome breakage'), ('MP:0003400', 'kinked neural tube'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0003396', 'abnormal embryonic hematopoiesis'), ('MP:0001700', 'abnormal embryo turning'), ('MP:0002151', 'abnormal neural tube morphology'), ('MP:0004573', 'absent limb buds'), ('MP:0002084', 'abnormal developmental patterning'), ('MP:0001688', 'abnormal somite development'), ('MP:0003674', 'oxidative stress'), ('MP:0013504', 'increased embryonic tissue cell apoptosis'), ('MP:0001726', 'abnormal allantois morphology'), ('GO:0000077PHENOTYPE', None), ('MP:0009657', 'failure of chorioallantoic fusion')]
MGI:3819487 Scarna6 []
MGI:104899 Mob4 []
MGI:5454701 Gm24924 []
MGI:95797 Gpi1 [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0002981', 'increased liver weight'), ('GO:0006096PHENOTYPE', None), ('MP:0011091', 'prenatal lethality, complete penetrance'), ('MP:0002085', 'abnormal embryonic tissue morphology'), ('MP:0010831', 'lethality, incomplete penetrance'), ('MP:0003656', 'abnormal erythrocyte physiology'), ('MP:0003657', 'abnormal erythrocyte osmotic lysis'), ('HP:0012379', None), ('MP:0004952', 'increased spleen weight'), ('HP:0001923', None), ('MP:0002833', 'increased heart weight'), ('GO:0001701PHENOTYPE', None), ('MP:0011095', 'embryonic lethality between implantation and placentation, complete penetrance'), ('GO:0042593PHENOTYPE', None), ('GO:0001707PHENOTYPE', None)]
MGI:3046938 Hrnr []
MGI:3045249 A830005F24Rik [('MP:0002135', 'abnormal kidney morphology'), ('HP:0001640', None)]
MGI:1196398 Champ1 []
MGI:5454116 Gm24339 []
MGI:1921772 Morc2a []
MGI:88474 Cox5a []
MGI:95628 Slc6a12 []
MGI:4821183 Trim12c []
MGI:3629885 Mir677 []
MGI:3642236 Rps23-ps2 []
MGI:1915968 Rnf166 []
MGI:3033475 Vmn1r65 []
MGI:3648836 Gm5822 []
MGI:2148802 Sec16b [('HP:0003146', None)]
MGI:2685483 Gsg1l []
MGI:3651112 Gm11889 []
MGI:95781 Gnb1 [('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0004948', 'abnormal neuronal precursor proliferation'), ('MP:0006254', 'thin cerebral cortex'), ('MP:0009937', 'abnormal neuron differentiation'), ('HP:0001322', None), ('MP:0008458', 'abnormal cortical ventricular zone morphology'), ('HP:0002033', None)]
MGI:88593 Cyp24a1 [('MP:0002135', 'abnormal kidney morphology'), ('MP:0002705', 'dilated renal tubules'), ('GO:0042359PHENOTYPE', None), ('MP:0011228', 'abnormal vitamin D level'), ('HP:0003072', None)]
MGI:4415007 Gm16587 []
MGI:3643792 Gm5449 []
MGI:1349452 Polr3e []
MGI:3641978 Gm10010 []
MGI:3704348 Gm10052 []
MGI:5455459 Gm25682 []
MGI:1919140 Mad2l2 [('HP:0000135', None), ('HP:0004325', None), ('HP:0008669', None), ('MP:0000746', 'weakness'), ('MP:0005389', 'reproductive system phenotype'), ('MP:0004901', 'decreased male germ cell number'), ('MP:0003202', 'abnormal neuron apoptosis'), ('MP:0012167', 'abnormal epigenetic regulation of gene expression'), ('MP:0005384', 'cellular phenotype'), ('MP:0000339', 'decreased enterocyte cell number'), ('HP:0010791', None), ('HP:0001511', None), ('MP:0002777', 'absent ovarian follicles'), ('MP:0001155', 'arrest of spermatogenesis'), ('MP:0008392', 'decreased primordial germ cell number'), ('MP:0003631', 'nervous system phenotype'), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('MP:0004200', 'decreased fetal size'), ('MP:0008393', 'absent primordial germ cells'), ('MP:0008489', 'slow postnatal weight gain'), ('MP:0001265', 'decreased body size'), ('MP:0004852', 'decreased testis weight')]
MGI:3709612 Gm14760 []
MGI:3650350 Gm14277 []
MGI:1338883 Gfpt2 [('HP:0006482', None), ('HP:0000517', None)]
MGI:1202295 Entpd6 [('MP:0010124', 'decreased bone mineral content'), ('MP:0003443', 'increased circulating glycerol level'), ('HP:0003330', None), ('HP:0008887', None), ('MP:0010123', 'increased bone mineral content'), ('MP:0003442', 'decreased circulating glycerol level')]
MGI:1924555 Wdr82 []
MGI:5454235 Gm24458 []
MGI:95666 Gbp2b [('MP:0002451', 'abnormal macrophage physiology'), ('HP:0002718', None), ('HP:0002014', None), ('HP:0001824', None), ('MP:0009788', 'increased susceptibility to bacterial infection induced morbidity/mortality')]
MGI:1289225 Tmem41b []
MGI:1891012 F12 [('MP:0003075', 'altered response to CNS ischemic injury'), ('GO:0005615PHENOTYPE', None), ('GO:0008233PHENOTYPE', None)]
MGI:3650211 Gm12732 []
MGI:3782988 Gm15540 []
MGI:1919006 Cpn2 []
MGI:1919216 Nkiras2 []
MGI:894649 Ppfibp2 []
MGI:3702953 Gm13594 []
MGI:3583899 A330069E16Rik []
MGI:1926421 Tcerg1 []
MGI:108020 Ear2 []
MGI:3650318 Gm14387 []
MGI:3646830 Gm5135 []
MGI:5456149 Gm26372 []
MGI:3045359 C130073F10Rik []
MGI:3802159 Gm15889 []
MGI:1096345 Gckr [('HP:0040216', None), ('MP:0005439', 'decreased glycogen level'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('HP:0000833', None)]
MGI:5455606 Gm25829 []
MGI:87995 Aldob [('MP:0001415', 'increased exploration in new environment'), ('HP:0010679', None), ('HP:0001397', None), ('MP:0003921', 'abnormal heart left ventricle morphology'), ('HP:0001392', None), ('MP:0003961', 'decreased lean body mass'), ('MP:0011967', 'increased or absent threshold for auditory brainstem response'), ('MP:0000603', 'pale liver'), ('MP:0001265', 'decreased body size'), ('HP:0012115', None)]
MGI:88285 Cbs [('MP:0002083', 'premature death'), ('HP:0004325', None), ('MP:0010098', 'abnormal retinal blood vessel pattern'), ('HP:0000568', None), ('HP:0000962', None), ('MP:0001613', 'abnormal vasodilation'), ('MP:0009018', 'short estrus'), ('MP:0001176', 'abnormal lung development'), ('MP:0008751', 'abnormal interleukin level'), ('MP:0014179', 'abnormal blood-retinal barrier function'), ('MP:0003070', 'increased vascular permeability'), ('MP:0001935', 'decreased litter size'), ('HP:0001397', None), ('MP:0002109', 'abnormal limb morphology'), ('MP:0009392', 'retinal gliosis'), ('MP:0003674', 'oxidative stress'), ('MP:0005639', 'hemosiderosis'), ('MP:0000652', 'enlarged sebaceous gland'), ('MP:0005186', 'increased circulating progesterone level'), ('MP:0008957', 'abnormal placenta junctional zone morphology'), ('GO:0001974PHENOTYPE', None), ('MP:0002182', 'abnormal astrocyte morphology'), ('HP:0001999', None), ('MP:0004126', 'thin hypodermis'), ('MP:0009020', 'prolonged metestrus'), ('HP:0012115', None), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('GO:0001958PHENOTYPE', None), ('MP:0000377', 'abnormal hair follicle morphology'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0002699', 'abnormal vitreous body morphology'), ('MP:0001286', 'abnormal eye development'), ('MP:0006076', 'abnormal circulating homocysteine level'), ('MP:0004921', 'decreased placenta weight'), ('MP:0010452', 'retina microaneurysm'), ('MP:0000528', 'delayed kidney development'), ('MP:0010771', 'integument phenotype'), ('MP:0001716', 'abnormal placenta labyrinth morphology'), ('MP:0002656', 'abnormal keratinocyte differentiation')]
MGI:5454894 Gm25117 []
MGI:1913509 Camk2n1 []
MGI:2138939 Nat10 [('MP:0011100', 'preweaning lethality, complete penetrance'), ('MP:0008044', 'increased NK cell number')]
MGI:1923616 Mtif3 []
MGI:2135611 Immp2l [('GO:0007420PHENOTYPE', None), ('GO:0006974PHENOTYPE', None), ('MP:0001132', 'absent mature ovarian follicles'), ('MP:0001921', 'reduced fertility'), ('MP:0001125', 'abnormal oocyte morphology'), ('MP:0010954', 'abnormal cellular respiration'), ('GO:0007283PHENOTYPE', None), ('HP:0000798', None), ('GO:0008015PHENOTYPE', None), ('MP:0001129', 'impaired ovarian folliculogenesis')]
MGI:1921063 Fam83e []
MGI:3646407 Gm7363 []
MGI:3643534 Angptl8 [('HP:0004325', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0011578', 'increased lipoprotein lipase activity'), ('HP:0003119', None), ('HP:0008887', None), ('MP:0008489', 'slow postnatal weight gain'), ('MP:0003976', 'decreased circulating VLDL triglyceride level')]
MGI:96777 Lgals1 [('HP:0011113', None), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0001973', 'increased thermal nociceptive threshold'), ('MP:0008566', 'increased interferon-gamma secretion'), ('HP:0040006', None), ('MP:0000972', 'abnormal mechanoreceptor morphology')]
MGI:1922646 Spp2 []
MGI:95286 Eed [('MP:0000371', 'diluted coat color'), ('MP:0002427', 'disproportionate dwarf'), ('GO:0016571PHENOTYPE', None)]
MGI:2668347 C8a []
MGI:1927186 Nt5c3 []
MGI:3647055 Gm8580 []
MGI:3781012 Gm2840 []
MGI:3648232 Gm7855 []
MGI:1931502 Snord82 []
MGI:1919489 Parp14 [('HP:0002720', None), ('MP:0008782', 'increased B cell apoptosis'), ('MP:0008182', 'decreased marginal zone B cell number')]
MGI:98818 Trap1a []
MGI:5454156 Gm24379 []
MGI:3651445 Gm13203 []
MGI:3648396 Gm6472 []
MGI:5663350 Gm43213 []
MGI:5454727 Gm24950 []
MGI:2442293 Usp28 [('MP:0009339', 'decreased splenocyte number'), ('MP:0005387', 'immune system phenotype')]
MGI:1341217 Uba3 [('MP:0002718', 'abnormal inner cell mass morphology'), ('GO:0051726PHENOTYPE', None), ('MP:0002085', 'abnormal embryonic tissue morphology'), ('MP:0003988', 'disorganized embryonic tissue'), ('GO:0000278PHENOTYPE', None), ('MP:0010380', 'abnormal inner cell mass apoptosis'), ('MP:0003693', 'abnormal blastocyst hatching'), ('MP:0011095', 'embryonic lethality between implantation and placentation, complete penetrance')]
MGI:3037679 Gm1821 []
ENSEMBL:ENSMUSG00000098615 Arvcf []
MGI:1914015 4933411K16Rik []
MGI:1352502 Limd1 [('MP:0001541', 'abnormal osteoclast physiology')]
MGI:5521099 Gtpbp4-ps2 []
MGI:3705843 Mup-ps17 []
MGI:1261856 Ankle2 []
MGI:1915602 Aspdh []
MGI:1860517 Rdh7 []
MGI:1917113 Ttc39b []
MGI:1915393 Herpud2 []
MGI:1917143 Coa7 []
MGI:2151104 Akr1c20 []
MGI:4938054 Gm17227 []
MGI:3801875 Gm15883 []
MGI:109581 S100a13 []
MGI:5452348 Gm22571 []
MGI:1347046 Rfwd2 [('MP:0000267', 'abnormal heart development'), ('HP:0004325', None), ('MP:0009219', 'increased prostate intraepithelial neoplasia incidence'), ('MP:0009321', 'increased histiocytic sarcoma incidence'), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('HP:0010784', None), ('HP:0100616', None), ('MP:0003607', 'abnormal prostate gland physiology')]
MGI:3646644 Gm6576 []
MGI:1202713 Rhag [('MP:0009642', 'abnormal blood homeostasis'), ('MP:0003656', 'abnormal erythrocyte physiology'), ('MP:0008810', 'increased circulating iron level'), ('GO:0048821PHENOTYPE', None)]
MGI:5452921 Gm23144 []
MGI:107547 Zfp101 []
MGI:3649792 Rpl27-ps2 []
MGI:5452524 Gm22747 []
MGI:3649506 Llph-ps1 []
MGI:3783061 Gm15616 []
MGI:4360046 Snora73a []
MGI:3651860 Gm11336 []
MGI:1915566 Apoo []
MGI:1916109 Smim1 []
MGI:3576090 Ugt1a8 []
MGI:3704338 Gm10642 []
MGI:1353620 Slamf6 [('MP:0008553', 'increased circulating tumor necrosis factor level'), ('HP:0011990', None)]
MGI:1917111 2010003K11Rik []
MGI:1196217 Tcaim [('HP:0003330', None)]
MGI:5434131 Gm20775 []
MGI:3646854 Gm8832 []
MGI:3643856 Gm7582 []
MGI:3646827 Rps6-ps2 []
MGI:5456238 Gm26461 []
MGI:3648525 Kansl2-ps []
MGI:2147897 Cenpi []
MGI:3652241 Gm11964 []
MGI:1920912 Apol7c []
MGI:5454316 Gm24539 []
MGI:3644080 Gm14650 []
MGI:5451813 Gm22036 []
MGI:2449316 Syne2 [('MP:0008511', 'thin retinal inner nuclear layer'), ('GO:0005635PHENOTYPE', None), ('MP:0008582', 'short photoreceptor inner segment'), ('MP:0003732', 'abnormal retinal outer plexiform layer morphology'), ('MP:0008587', 'short photoreceptor outer segment'), ('GO:0034504PHENOTYPE', None), ('MP:0005391', 'vision/eye phenotype'), ('HP:0000962', None), ('MP:0004022', 'abnormal cone electrophysiology'), ('MP:0006068', 'abnormal horizontal cell morphology'), ('MP:0005369', 'muscle phenotype'), ('MP:0005547', 'abnormal Muller cell morphology'), ('GO:0006998PHENOTYPE', None), ('MP:0006072', 'abnormal retinal apoptosis'), ('MP:0002655', 'abnormal keratinocyte morphology'), ('MP:0001053', 'abnormal neuromuscular synapse morphology'), ('MP:0004021', 'abnormal rod electrophysiology')]
MGI:1923805 Mmaa []
MGI:1930124 Apom [('MP:0003983', 'decreased cholesterol level'), ('HP:0100878', None), ('HP:0003233', None)]
MGI:1914492 Gemin6 []
MGI:1342058 B4galnt2 [('HP:0010975', None)]
MGI:1918367 Rbbp5 []
MGI:4439831 Gm16907 []
MGI:101775 Cd80 [('MP:0001800', 'abnormal humoral immune response'), ('MP:0008497', 'decreased IgG2b level'), ('MP:0004804', 'decreased susceptibility to autoimmune diabetes'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('HP:0004313', None), ('MP:0005042', 'abnormal level of surface class II molecules'), ('HP:0011839', None), ('MP:0008207', 'decreased B-2 B cell number'), ('MP:0008495', 'decreased IgG1 level'), ('MP:0004031', 'insulitis'), ('MP:0008498', 'decreased IgG3 level')]
MGI:3804971 Gm5561 []
MGI:892001 Slc22a6 [('MP:0006272', 'abnormal urine organic anion level'), ('MP:0008874', 'decreased physiological sensitivity to xenobiotic')]
MGI:88583 Cyp11b1 [('MP:0011546', 'increased urine progesterone level'), ('MP:0011541', 'decreased urine aldosterone level'), ('MP:0011550', 'decreased urine corticosterone level'), ('MP:0008294', 'abnormal adrenal gland zona fasciculata morphology'), ('HP:0008221', None), ('MP:0009092', 'endometrium hyperplasia'), ('MP:0002833', 'increased heart weight'), ('HP:0100878', None), ('HP:0008222', None), ('HP:0000833', None)]
MGI:2442326 Zbtb1 [('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('HP:0005403', None), ('MP:0000703', 'abnormal thymus morphology'), ('MP:0002145', 'abnormal T cell differentiation'), ('GO:0030183PHENOTYPE', None), ('MP:0010136', 'decreased DN4 thymocyte number'), ('HP:0002846', None), ('HP:0010978', None), ('MP:0010134', 'decreased DN3 thymocyte number'), ('GO:0033077PHENOTYPE', None), ('MP:0010132', 'decreased DN2 thymocyte number'), ('HP:0005404', None), ('MP:0005092', 'decreased double-positive T cell number'), ('HP:0010976', None)]
MGI:1916296 Isca1 [('MP:0013278', 'decreased fasted circulating glucose level'), ('MP:0001417', 'decreased exploration in new environment'), ('MP:0013293', 'embryonic lethality prior to tooth bud stage'), ('MP:0011275', 'abnormal behavioral response to light'), ('MP:0002757', 'decreased vertical activity')]
MGI:1353563 Snai3 []
MGI:3644565 Gm8730 []
MGI:2442610 A330035P11Rik []
MGI:1919290 Cdhr5 []
MGI:1346344 Nr0b2 [('MP:0002646', 'increased intestinal cholesterol absorption'), ('MP:0004884', 'abnormal testis physiology'), ('MP:0005365', 'abnormal bile salt homeostasis'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0004928', 'increased epididymis weight'), ('MP:0009355', 'increased liver triglyceride level'), ('HP:0003233', None), ('MP:0000183', 'decreased circulating LDL cholesterol level'), ('MP:0010180', 'increased susceptibility to weight loss'), ('MP:0004852', 'decreased testis weight'), ('MP:0004789', 'increased bile salt level'), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0003146', None)]
MGI:1918632 Pex1 [('MP:0005282', 'decreased fatty acid level'), ('HP:0001397', None), ('HP:0001408', None), ('MP:0006084', 'abnormal circulating phospholipid level'), ('MP:0004022', 'abnormal cone electrophysiology'), ('MP:0005365', 'abnormal bile salt homeostasis'), ('MP:0004021', 'abnormal rod electrophysiology')]
MGI:3714859 Cyp3a41b []
MGI:96108 Hlf []
MGI:1888505 Retnlb [('MP:0008561', 'decreased tumor necrosis factor secretion'), ('MP:0005026', 'decreased susceptibility to parasitic infection'), ('MP:0001663', 'abnormal digestive system physiology'), ('MP:0008499', 'increased IgG1 level'), ('MP:0008537', 'increased susceptibility to induced colitis')]
MGI:1913489 Rsrc2 []
MGI:3649326 Gm14448 []
MGI:3649364 Atp5l2-ps []
MGI:1913697 Mgst3 [('HP:0005518', None)]
MGI:2685505 C2cd4d []
MGI:1920999 Ttc7 [('MP:0010176', 'dacryocytosis'), ('MP:0002082', 'postnatal lethality'), ('MP:0009395', 'increased nucleated erythrocyte cell number'), ('HP:0004325', None), ('MP:0011242', 'increased fetal derived definitive erythrocyte cell number'), ('MP:0008476', 'increased spleen red pulp amount'), ('HP:0001006', None), ('MP:0008479', 'decreased spleen white pulp amount'), ('MP:0001586', 'abnormal erythrocyte cell number'), ('HP:0001923', None), ('MP:0002594', 'low mean erythrocyte cell number'), ('MP:0001246', 'mixed cellular infiltration to dermis'), ('MP:0011413', 'colorless urine'), ('MP:0008810', 'increased circulating iron level'), ('HP:0040162', None), ('MP:0002655', 'abnormal keratinocyte morphology'), ('MP:0000245', 'abnormal erythropoiesis'), ('HP:0001595', None), ('HP:0000980', None), ('HP:0001877', None), ('MP:0001243', 'abnormal dermal layer morphology'), ('HP:0005548', None), ('HP:0011273', None), ('MP:0005097', 'polychromatophilia'), ('MP:0004969', 'pale kidney'), ('HP:0004447', None), ('MP:0000472', 'abnormal stomach non-glandular epithelium morphology'), ('HP:0002240', None), ('MP:0000607', 'abnormal hepatocyte morphology'), ('MP:0002123', 'abnormal definitive hematopoiesis'), ('HP:0012115', None), ('HP:0001927', None), ('HP:0001036', None), ('MP:0002591', 'decreased mean corpuscular volume'), ('MP:0010771', 'integument phenotype'), ('GO:0030097PHENOTYPE', None)]
MGI:3644887 Gm5812 []
MGI:5454222 Gm24445 []
MGI:3704357 Gm9855 []
MGI:1351634 Abcc6 [('MP:0005239', 'abnormal Bruch membrane morphology'), ('HP:0003761', None), ('MP:0010234', 'abnormal vibrissa follicle morphology'), ('MP:0002838', 'decreased susceptibility to dystrophic cardiac calcinosis'), ('HP:0007862', None), ('MP:0002839', 'increased susceptibility to dystrophic cardiac calcinosis')]
MGI:99175 Zfp28 []
MGI:3649924 Gm13237 []
MGI:2137092 Eefsec []
MGI:3783082 Gm15638 []
MGI:1919356 Jmjd8 [('HP:0005518', None)]
MGI:3647036 Gm5931 []
MGI:3651480 Gm11759 []
MGI:1330304 Vps52 [('MP:0002085', 'abnormal embryonic tissue morphology'), ('MP:0001723', 'disorganized yolk sac vascular plexus'), ('MP:0011092', 'embryonic lethality, complete penetrance')]
MGI:1916964 Tfpt []
MGI:97350 Nkx2-5 [('MP:0006126', 'abnormal cardiac outflow tract development'), ('MP:0000298', 'absent atrioventricular cushions'), ('HP:0001903', None), ('HP:0012722', None), ('GO:0001947PHENOTYPE', None), ('MP:0005385', 'cardiovascular system phenotype'), ('HP:0000969', None), ('GO:0055005PHENOTYPE', None), ('MP:0001722', 'pale yolk sac'), ('MP:0002086', 'abnormal extraembryonic tissue morphology'), ('MP:0001625', 'cardiac hypertrophy'), ('GO:0060038PHENOTYPE', None), ('HP:0009555', None), ('GO:0060048PHENOTYPE', None), ('MP:0002747', 'abnormal aortic valve morphology'), ('HP:0001698', None), ('MP:0002085', 'abnormal embryonic tissue morphology'), ('HP:0003826', None), ('MP:0004114', 'abnormal atrioventricular node morphology'), ('MP:0001633', 'poor circulation'), ('HP:0001644', None), ('HP:0001750', None), ('GO:0007507PHENOTYPE', None), ('MP:0001689', 'incomplete somite formation'), ('MP:0011205', 'excessive folding of visceral yolk sac'), ('HP:0001719', None), ('HP:0001647', None), ('MP:0000282', 'abnormal interatrial septum morphology'), ('MP:0004117', 'abnormal atrioventricular bundle morphology'), ('MP:0000729', 'abnormal myogenesis'), ('MP:0005598', 'decreased ventricle muscle contractility'), ('MP:0003649', 'decreased heart right ventricle size'), ('HP:0001660', None), ('MP:0000278', 'abnormal myocardial fiber morphology'), ('MP:0004787', 'abnormal dorsal aorta morphology'), ('MP:0004124', 'abnormal Purkinje fiber morphology'), ('MP:0003872', 'absent heart right ventricle'), ('MP:0003984', 'embryonic growth retardation'), ('GO:0001570PHENOTYPE', None), ('MP:0006113', 'abnormal heart septum morphology'), ('MP:0003920', 'abnormal heart right ventricle morphology'), ('HP:0000961', None), ('HP:0001789', None), ('MP:0003140', 'dilated heart atrium')]
MGI:95895 H2-Aa [('MP:0000702', 'enlarged lymph nodes'), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('MP:0008078', 'increased CD8-positive, alpha-beta T cell number')]
MGI:5454177 Gm24400 []
MGI:3780551 Gm2383 []
MGI:2146156 Espl1 [('MP:0012570', 'increased mammary gland tumor incidence in breeding females'), ('MP:0002052', 'decreased tumor incidence'), ('MP:0000630', 'mammary gland hyperplasia'), ('MP:0004957', 'abnormal blastocyst morphology'), ('MP:0004024', 'aneuploidy'), ('MP:0008393', 'absent primordial germ cells'), ('HP:0008669', None), ('MP:0000333', 'decreased bone marrow cell number'), ('MP:0006271', 'abnormal involution of the mammary gland'), ('MP:0011411', 'abnormal gonadal ridge morphology'), ('MP:0004966', 'abnormal inner cell mass proliferation'), ('MP:0008866', 'chromosomal instability'), ('MP:0000607', 'abnormal hepatocyte morphology'), ('MP:0011092', 'embryonic lethality, complete penetrance'), ('MP:0008390', 'abnormal primordial germ cell proliferation')]
MGI:2144585 Slc16a6 []
MGI:1923388 Ccdc138 []
MGI:1917059 1810046K07Rik []
MGI:3710580 Gm9800 []
MGI:1928138 Mrps23 []
MGI:1298204 Ppt1 [('MP:0001513', 'limb grasping'), ('GO:0008344PHENOTYPE', None), ('HP:0004324', None), ('GO:0044257PHENOTYPE', None), ('HP:0001743', None), ('HP:0001392', None), ('HP:0012443', None), ('MP:0002175', 'decreased brain weight'), ('GO:0008306PHENOTYPE', None), ('MP:0000788', 'abnormal cerebral cortex morphology'), ('GO:0008474PHENOTYPE', None), ('GO:0044265PHENOTYPE', None), ('MP:0008713', 'abnormal cytokine level'), ('HP:0002446', None), ('GO:0005764PHENOTYPE', None), ('MP:0002135', 'abnormal kidney morphology'), ('HP:0010831', None), ('HP:0000718', None), ('MP:0003241', 'loss of cortex neurons')]
MGI:1923764 Tmub1 [('MP:0008853', 'decreased abdominal adipose tissue amount'), ('HP:0012311', None), ('MP:0001501', 'abnormal sleep pattern')]
MGI:3650962 Mup6 []
MGI:5455456 Gm25679 []
MGI:95679 Gpd1 [('GO:0006116PHENOTYPE', None), ('MP:0005378', 'growth/size/body region phenotype'), ('MP:0005389', 'reproductive system phenotype')]
MGI:2451333 Khnyn []
MGI:3783040 Gm15593 []
MGI:1918920 Acy3 []
MGI:5456164 Gm26387 []
MGI:3652074 Gm11367 []
MGI:4422070 n-R5s205 []
MGI:1098258 Kif15 []
MGI:1923028 Them4 [('MP:0005389', 'reproductive system phenotype')]
MGI:3649491 Gm11852 []
MGI:1914691 Isoc2b []
MGI:96692 Krt18 [('GO:0097191PHENOTYPE', None), ('HP:0002240', None)]
MGI:2676630 Nlrp12 [('MP:0005616', 'decreased susceptibility to type IV hypersensitivity reaction'), ('GO:0071345PHENOTYPE', None), ('HP:0040238', None), ('HP:0001875', None)]
MGI:1918115 Lnpk [('HP:0003010', None), ('HP:0003251', None), ('HP:0005736', None), ('MP:0002109', 'abnormal limb morphology'), ('MP:0004355', 'short radius'), ('GO:0007596PHENOTYPE', None), ('MP:0003072', 'abnormal metatarsal bone morphology'), ('MP:0000060', 'delayed bone ossification'), ('MP:0002187', 'abnormal fibula morphology')]
MGI:3643566 Gm5617 []
MGI:2139354 Arfgef2 [('HP:0002269', None), ('HP:0004298', None), ('MP:0003631', 'nervous system phenotype'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance')]
MGI:2443362 Lax1 [('MP:0002359', 'abnormal spleen germinal center morphology'), ('HP:0003212', None), ('MP:0001828', 'abnormal T cell activation'), ('MP:0008826', 'abnormal splenic cell ratio'), ('MP:0008499', 'increased IgG1 level')]
MGI:2685466 1700024P16Rik []
MGI:96177 Hoxa5 [('GO:0001501PHENOTYPE', None), ('MP:0003120', 'abnormal tracheal cartilage morphology'), ('MP:0010993', 'decreased surfactant secretion'), ('MP:0009018', 'short estrus'), ('MP:0001179', 'thick pulmonary interalveolar septum'), ('GO:0007389PHENOTYPE', None), ('MP:0009247', 'meteorism'), ('MP:0010943', 'abnormal bronchus epithelium morphology'), ('MP:0002267', 'abnormal bronchiole morphology'), ('MP:0013497', 'trachea occlusion'), ('MP:0001927', 'abnormal estrous cycle'), ('MP:0005122', 'increased circulating thyroid-stimulating hormone level'), ('HP:0000826', None), ('MP:0004615', 'cervical vertebral transformation'), ('MP:0011034', 'impaired branching involved in respiratory bronchiole morphogenesis'), ('MP:0004780', 'abnormal surfactant secretion'), ('GO:0060536PHENOTYPE', None), ('MP:0004551', 'decreased tracheal cartilage ring number'), ('GO:0030324PHENOTYPE', None), ('MP:0008146', 'asymmetric sternocostal joints'), ('GO:0060644PHENOTYPE', None), ('MP:0005388', 'respiratory system phenotype'), ('HP:0040006', None), ('HP:0000772', None), ('MP:0010900', 'abnormal pulmonary interalveolar septum morphology'), ('MP:0011088', 'neonatal lethality, incomplete penetrance'), ('MP:0000054', 'delayed ear emergence'), ('GO:0007585PHENOTYPE', None), ('HP:0000138', None), ('GO:0009952PHENOTYPE', None), ('HP:0002777', None), ('MP:0001183', 'overexpanded pulmonary alveoli'), ('HP:0005815', None), ('MP:0005629', 'abnormal lung weight'), ('MP:0009020', 'prolonged metestrus'), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('HP:0001508', None), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('GO:0030878PHENOTYPE', None), ('MP:0010911', 'abnormal pulmonary acinus morphology'), ('MP:0010082', 'sternebra fusion'), ('MP:0010810', 'increased type II pneumocyte number'), ('HP:0100750', None), ('MP:0011024', 'abnormal branching involved in lung morphogenesis'), ('GO:0048704PHENOTYPE', None), ('MP:0008245', 'abnormal alveolar macrophage morphology'), ('MP:0001290', 'delayed eyelid opening')]
MGI:104992 Crybb1 []
MGI:96114 Hmgb1-ps8 []
MGI:1932389 Tlr9 [('MP:0005465', 'abnormal T-helper 1 physiology'), ('GO:0006955PHENOTYPE', None), ('MP:0008769', 'abnormal plasmacytoid dendritic cell physiology'), ('HP:0011113', None), ('MP:0008561', 'decreased tumor necrosis factor secretion'), ('MP:0005387', 'immune system phenotype'), ('MP:0008565', 'decreased interferon-beta secretion'), ('HP:0010978', None), ('MP:0002418', 'increased susceptibility to viral infection'), ('HP:0012648', None), ('MP:0002376', 'abnormal dendritic cell physiology')]
MGI:97372 Npr2 [('HP:0011028', None), ('MP:0000097', 'short maxilla'), ('MP:0002427', 'disproportionate dwarf'), ('HP:0004325', None), ('MP:0005385', 'cardiovascular system phenotype'), ('MP:0001921', 'reduced fertility'), ('MP:0004386', 'enlarged interparietal bone'), ('HP:0003026', None), ('MP:0009677', 'abnormal spinal cord dorsal column morphology'), ('HP:0002983', None), ('HP:0003097', None), ('HP:0005105', None), ('HP:0000134', None), ('HP:0003270', None), ('MP:0006398', 'increased long bone epiphyseal plate size'), ('MP:0000165', 'abnormal long bone hypertrophic chondrocyte zone'), ('MP:0002637', 'small uterus'), ('MP:0010029', 'abnormal basicranium morphology'), ('MP:0004421', 'enlarged parietal bone'), ('MP:0005390', 'skeleton phenotype'), ('MP:0009088', 'thin uterine horn'), ('GO:0060348PHENOTYPE', None), ('HP:0002808', None), ('HP:0010504', None), ('HP:0040006', None), ('HP:0004422', None), ('HP:0009121', None), ('HP:0100671', None), ('HP:0000689', None), ('MP:0009861', 'abnormal pyloric sphincter morphology'), ('HP:0002766', None), ('HP:0000267', None), ('MP:0000592', 'short tail'), ('MP:0001392', 'abnormal locomotor behavior'), ('HP:0010824', None), ('HP:0003022', None), ('MP:0003662', 'abnormal long bone epiphyseal plate proliferative zone'), ('MP:0004470', 'small nasal bone'), ('HP:0002578', None), ('MP:0004595', 'abnormal mandibular condyloid process morphology'), ('MP:0000438', 'abnormal cranium morphology'), ('MP:0009009', 'absent estrous cycle'), ('HP:0001508', None), ('HP:0001291', None), ('HP:0005736', None), ('MP:0010155', 'abnormal intestine physiology'), ('MP:0004673', 'splayed ribs'), ('MP:0002113', 'abnormal skeleton development'), ('MP:0004607', 'abnormal cervical atlas morphology'), ('MP:0001265', 'decreased body size'), ('MP:0008272', 'abnormal endochondral bone ossification'), ('MP:0008770', 'decreased survivor rate'), ('HP:0011063', None), ('MP:0002657', 'chondrodystrophy')]
MGI:95564 Fmr1 [('MP:0001363', 'increased anxiety-related response'), ('MP:0008871', 'abnormal ovarian follicle number'), ('HP:0000137', None), ('MP:0001458', 'abnormal object recognition memory'), ('MP:0009456', 'impaired cued conditioning behavior'), ('MP:0000877', 'abnormal Purkinje cell morphology'), ('MP:0002574', 'increased vertical activity'), ('MP:0002062', 'abnormal associative learning'), ('MP:0001364', 'decreased anxiety-related response'), ('MP:0009940', 'abnormal hippocampus pyramidal cell morphology'), ('MP:0005391', 'vision/eye phenotype'), ('MP:0003008', 'enhanced long term potentiation'), ('MP:0000947', 'convulsive seizures'), ('MP:0001463', 'abnormal spatial learning'), ('MP:0012144', 'decreased b wave amplitude'), ('MP:0001529', 'abnormal vocalization'), ('MP:0009363', 'abnormal secondary ovarian follicle morphology'), ('HP:0007973', None), ('MP:0005168', 'abnormal female meiosis'), ('HP:0004324', None), ('MP:0000812', 'abnormal dentate gyrus morphology'), ('MP:0012143', 'decreased a wave amplitude'), ('HP:0000138', None), ('MP:0002063', 'abnormal learning/memory/conditioning'), ('MP:0001415', 'increased exploration in new environment'), ('MP:0003702', 'abnormal chromosome morphology'), ('HP:0012443', None), ('MP:0004753', 'abnormal miniature excitatory postsynaptic currents'), ('MP:0009141', 'increased prepulse inhibition'), ('MP:0009937', 'abnormal neuron differentiation'), ('MP:0001360', 'abnormal social investigation'), ('MP:0002680', 'decreased corpora lutea number'), ('MP:0004851', 'increased testis weight'), ('GO:0007417PHENOTYPE', None), ('MP:0009712', 'impaired conditioned place preference behavior')]
MGI:5454704 Gm24927 []
MGI:3645626 Gm16433 []
MGI:3645402 Gm6733 []
MGI:5452049 Gm22272 []
MGI:3645731 Gm5641 []
MGI:3649615 Gm13443 []
MGI:1926262 Irgm2 []
MGI:5452146 Gm22369 []
MGI:3773841 Mfsd4b3 []
MGI:1202882 Rhd [('MP:0002591', 'decreased mean corpuscular volume'), ('MP:0002874', 'decreased hemoglobin content'), ('MP:0001191', 'abnormal skin condition'), ('MP:0005642', 'decreased mean corpuscular hemoglobin concentration')]
MGI:2387602 Cd300e [('MP:0005560', 'decreased circulating glucose level'), ('MP:0002059', 'abnormal seminal vesicle morphology'), ('MP:0004931', 'enlarged epididymis')]
MGI:2685565 Nup62cl []
MGI:3783067 Gm15622 []
MGI:3649359 Gm13886 []
MGI:1919266 1600002H07Rik []
MGI:1925579 2310010J17Rik []
MGI:2663233 Muc6 []
MGI:102944 Ube2b [('MP:0009230', 'abnormal sperm head morphology'), ('MP:0001510', 'abnormal coat appearance'), ('HP:0012153', None), ('MP:0005169', 'abnormal male meiosis'), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('HP:0008669', None), ('HP:0001288', None), ('MP:0004852', 'decreased testis weight'), ('MP:0006379', 'abnormal spermatocyte morphology')]
MGI:3648443 Gm7847 []
MGI:3644644 Gm5883 []
MGI:104664 Tcf15 [('GO:0042755PHENOTYPE', None), ('GO:0007517PHENOTYPE', None), ('GO:0043588PHENOTYPE', None), ('HP:0000973', None), ('GO:0001756PHENOTYPE', None), ('HP:0003468', None), ('HP:0004298', None), ('GO:0045198PHENOTYPE', None), ('MP:0004263', 'abnormal limb posture'), ('HP:0002098', None), ('MP:0002280', 'abnormal intercostal muscle morphology'), ('HP:0000921', None), ('GO:0009952PHENOTYPE', None), ('HP:0000892', None), ('MP:0004073', 'caudal body truncation'), ('GO:0003016PHENOTYPE', None), ('MP:0008277', 'abnormal sternum ossification'), ('MP:0000080', 'abnormal exoccipital bone morphology'), ('HP:0000902', None), ('HP:0000772', None)]
MGI:3643873 Gm6594 []
MGI:1921737 5430405H02Rik []
MGI:95936 H2-Q7 []
MGI:5454456 Gm24679 []
MGI:1096341 E2f2 [('MP:0000313', 'abnormal cell death'), ('HP:0002090', None), ('MP:0002145', 'abnormal T cell differentiation'), ('HP:0000099', None), ('MP:0005350', 'increased susceptibility to autoimmune disorder'), ('MP:0004952', 'increased spleen weight'), ('MP:0003944', 'abnormal T cell subpopulation ratio'), ('MP:0011294', 'renal glomerulus hypertrophy'), ('HP:0011017', None), ('MP:0004762', 'increased anti-double stranded DNA antibody level')]
MGI:3645972 Sult3a2 []
MGI:3651135 Gm12909 []
MGI:3646434 Gm4788 []
MGI:1923455 Stard3nl []
MGI:5454201 Gm24424 []
MGI:1921080 Eif3k []
MGI:2444899 5031439G07Rik []
MGI:1919311 2010111I01Rik []
MGI:1920179 Dip2c []
MGI:1891713 Habp4 []
MGI:3649539 Gm13880 []
MGI:3036263 Zfp976 []
MGI:3649578 Gm13623 []
MGI:1919352 Dusp11 [('HP:0100689', None), ('MP:0001258', 'decreased body length')]
MGI:98797 Tpi1 [('HP:0012379', None), ('HP:0001878', None), ('MP:0000208', 'decreased hematocrit'), ('MP:0011096', 'embryonic lethality between implantation and somite formation, complete penetrance')]
MGI:1913513 1810014B01Rik []
MGI:1861676 Nme6 [('MP:0003984', 'embryonic growth retardation'), ('MP:0013292', 'embryonic lethality prior to organogenesis'), ('MP:0001697', 'abnormal embryo size'), ('MP:0011100', 'preweaning lethality, complete penetrance')]
MGI:2449929 Il1f9 []
MGI:1913288 Sdhaf3 []
MGI:3783232 Gm15790 []
MGI:3648120 Gm5637 []
MGI:1923666 Znrd1as []
MGI:3769707 Cyp3a59 []
MGI:3780787 Gm2619 []
MGI:1860079 Olfr70 []
MGI:1346527 Psmb8 [('MP:0001838', 'defective intracellular transport of class I molecules')]
MGI:3649746 Gm12230 []
MGI:5531106 Mir6963 []
MGI:95629 Slc6a13 []
MGI:2178743 Stab2 [('MP:0009642', 'abnormal blood homeostasis')]
MGI:3644670 Gm6142 []
MGI:3781329 Gm3150 []
MGI:3648278 Gm7541 []
MGI:5454607 Gm24830 []
MGI:3649931 Gm11361 []
MGI:2146052 Agxt2 [('MP:0005590', 'increased vasodilation'), ('HP:0003112', None)]
MGI:1101758 H3f3a-ps2 []
MGI:3651322 Gm12328 []
MGI:3708691 Gm2564 []
MGI:1333825 Dgat1 [('MP:0010026', 'decreased liver cholesterol level'), ('MP:0011232', 'abnormal vitamin A level'), ('MP:0001212', 'skin lesions'), ('MP:0001222', 'epidermal hyperplasia'), ('MP:0004047', 'abnormal milk composition'), ('HP:0004325', None), ('MP:0006270', 'abnormal mammary gland growth during lactation'), ('MP:0005459', 'decreased percent body fat/body weight'), ('MP:0008858', 'abnormal hair cycle anagen phase'), ('MP:0009504', 'abnormal mammary gland epithelium morphology'), ('MP:0005289', 'increased oxygen consumption'), ('HP:0000752', None), ('MP:0009356', 'decreased liver triglyceride level'), ('MP:0001191', 'abnormal skin condition'), ('MP:0000427', 'abnormal hair cycle'), ('MP:0000628', 'abnormal mammary gland development'), ('MP:0012075', 'impaired mammary gland growth during pregnancy')]
MGI:1915720 Impad1 [('HP:0002795', None), ('HP:0005792', None), ('HP:0004324', None), ('GO:0001501PHENOTYPE', None), ('GO:0002063PHENOTYPE', None), ('GO:0001958PHENOTYPE', None), ('HP:0009121', None), ('MP:0002427', 'disproportionate dwarf'), ('HP:0000879', None), ('HP:0003826', None), ('HP:0001547', None), ('HP:0002098', None), ('MP:0005390', 'skeleton phenotype'), ('HP:0000267', None), ('HP:0008873', None), ('MP:0000071', 'axial skeleton hypoplasia'), ('MP:0006397', 'disorganized long bone epiphyseal plate'), ('MP:0011087', 'neonatal lethality, complete penetrance'), ('GO:0009791PHENOTYPE', None)]
MGI:5455884 Gm26107 []
MGI:3652203 Gm13604 []
MGI:1918943 Colec11 []
MGI:894297 Clta []
MGI:1276535 Ncoa3 [('MP:0003355', 'decreased ovulation rate'), ('MP:0005379', 'endocrine/exocrine gland phenotype'), ('HP:0009124', None), ('MP:0012106', 'impaired exercise endurance'), ('MP:0002427', 'disproportionate dwarf'), ('MP:0009006', 'prolonged estrous cycle'), ('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('GO:0035264PHENOTYPE', None), ('MP:0005290', 'decreased oxygen consumption'), ('MP:0005076', 'abnormal cell differentiation'), ('HP:0006482', None), ('MP:0002401', 'abnormal lymphopoiesis'), ('HP:0000823', None), ('MP:0006319', 'abnormal epididymal fat pad morphology'), ('MP:0009136', 'decreased brown fat cell size'), ('MP:0005181', 'decreased circulating estradiol level'), ('HP:0030759', None), ('MP:0005289', 'increased oxygen consumption'), ('MP:0002356', 'abnormal spleen red pulp morphology'), ('HP:0000855', None), ('HP:0040216', None), ('MP:0002696', 'decreased circulating glucagon level'), ('HP:0001974', None), ('HP:0004324', None), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('HP:0003074', None), ('MP:0009133', 'decreased white fat cell size'), ('MP:0009341', 'decreased splenocyte apoptosis'), ('MP:0005154', 'increased B cell proliferation'), ('MP:0005560', 'decreased circulating glucose level'), ('MP:0008705', 'increased interleukin-6 secretion'), ('MP:0008483', 'increased spleen germinal center size'), ('MP:0008596', 'increased circulating interleukin-6 level'), ('MP:0003402', 'decreased liver weight'), ('HP:0011017', None), ('GO:0060068PHENOTYPE', None), ('MP:0002169', 'no abnormal phenotype detected'), ('HP:0010683', None), ('MP:0008074', 'increased CD4-positive, alpha beta T cell number'), ('HP:0001508', None), ('MP:0008735', 'increased susceptibility to endotoxin shock'), ('HP:0000369', None), ('MP:0008553', 'increased circulating tumor necrosis factor level'), ('HP:0002045', None), ('MP:0011049', 'impaired adaptive thermogenesis'), ('MP:0008641', 'increased circulating interleukin-1 beta level'), ('MP:0011630', 'increased mitochondria size'), ('HP:0008887', None), ('HP:0002846', None), ('GO:0048589PHENOTYPE', None), ('MP:0008658', 'decreased interleukin-1 beta secretion'), ('MP:0001265', 'decreased body size'), ('MP:0010378', 'increased respiratory quotient'), ('MP:0001780', 'decreased brown adipose tissue amount'), ('HP:0002665', None), ('MP:0000352', 'decreased cell proliferation'), ('MP:0000628', 'abnormal mammary gland development'), ('MP:0005292', 'improved glucose tolerance')]
MGI:3772572 Xaf1 []
MGI:1922471 Dpp3 []
MGI:5452893 Gm23116 []
MGI:1351627 Pdhx []
MGI:1920081 Dhx30 [('MP:0004180', 'failure of initiation of embryo turning')]
MGI:3651835 Gm13994 []
MGI:108450 Adcy9 [('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('HP:0003113', None)]
MGI:3652298 Gm11349 []
MGI:3648635 Gm8815 []
MGI:3782393 Gm4217 []
MGI:104982 Cebpg [('MP:0001183', 'overexpanded pulmonary alveoli'), ('MP:0005070', 'impaired natural killer cell mediated cytotoxicity'), ('MP:0011088', 'neonatal lethality, incomplete penetrance')]
MGI:3650608 Gm11942 []
MGI:2136934 Clec2h []
MGI:1329005 Slfn3 []
MGI:1915921 Pcyt2 [('MP:0011092', 'embryonic lethality, complete penetrance'), ('MP:0002118', 'abnormal lipid homeostasis'), ('MP:0010027', 'increased liver cholesterol level'), ('MP:0009355', 'increased liver triglyceride level')]
MGI:1918937 Tmem25 []
MGI:96031 Hc [('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0001952', 'increased airway responsiveness'), ('MP:0005166', 'decreased susceptibility to injury'), ('MP:0011471', 'decreased urine creatinine level'), ('MP:0002833', 'increased heart weight'), ('MP:0011396', 'abnormal sleep behavior'), ('MP:0001257', 'increased body length')]
MGI:3650694 Gm11979 []
MGI:3614797 Ripply1 [('MP:0003048', 'abnormal cervical vertebrae morphology'), ('GO:0060349PHENOTYPE', None)]
MGI:3652153 Gm12568 []
MGI:97517 Cdk17 []
MGI:3588218 F830016B08Rik []
MGI:102483 mt-Tk [('HP:0001324', None), ('MP:0010955', 'abnormal respiratory electron transport chain'), ('MP:0001258', 'decreased body length')]
MGI:1341190 Hmg20b []
MGI:3651366 Gm13132 []
MGI:108520 Fzd4 [('MP:0002083', 'premature death'), ('GO:0030947PHENOTYPE', None), ('MP:0004362', 'cochlear hair cell degeneration'), ('MP:0004404', 'cochlear outer hair cell degeneration'), ('MP:0004398', 'cochlear inner hair cell degeneration'), ('MP:0004368', 'abnormal stria vascularis vasculature morphology'), ('GO:0031987PHENOTYPE', None), ('HP:0000568', None), ('MP:0005602', 'decreased angiogenesis'), ('MP:0009619', 'abnormal optokinetic reflex'), ('HP:0001321', None), ('MP:0002792', 'abnormal retinal vasculature morphology'), ('MP:0000880', 'decreased Purkinje cell number'), ('HP:0007033', None), ('HP:0000573', None), ('MP:0004363', 'stria vascularis degeneration'), ('MP:0005185', 'decreased circulating progesterone level'), ('MP:0000469', 'abnormal esophageal squamous epithelium morphology'), ('MP:0001505', 'hunched posture'), ('HP:0001892', None), ('GO:0042701PHENOTYPE', None), ('HP:0012372', None), ('HP:0200057', None), ('GO:0001568PHENOTYPE', None), ('MP:0000259', 'abnormal vascular development'), ('HP:0008222', None), ('MP:0002169', 'no abnormal phenotype detected'), ('GO:0007605PHENOTYPE', None), ('MP:0000886', 'abnormal cerebellar granule layer morphology'), ('MP:0009402', 'decreased skeletal muscle fiber diameter'), ('MP:0003356', 'impaired luteinization'), ('HP:0000365', None), ('HP:0001789', None)]
MGI:99260 Prkci [('MP:0012104', 'small amniotic cavity'), ('MP:0003949', 'abnormal circulating lipid level'), ('MP:0002083', 'premature death'), ('MP:0008140', 'podocyte foot process effacement'), ('HP:0003259', None), ('HP:0000568', None), ('MP:0011190', 'thick embryonic epiblast'), ('MP:0008060', 'abnormal podocyte slit diaphragm morphology'), ('MP:0004185', 'abnormal adipocyte glucose uptake'), ('MP:0004559', 'small allantois'), ('MP:0011402', 'renal cast'), ('MP:0011257', 'abnormal head fold morphology'), ('MP:0005669', 'increased circulating leptin level'), ('MP:0005325', 'abnormal renal glomerulus morphology'), ('MP:0011092', 'embryonic lethality, complete penetrance'), ('MP:0000358', 'abnormal cell morphology'), ('HP:0003233', None), ('MP:0012081', 'absent heart tube'), ('HP:0003138', None), ('MP:0000270', 'abnormal heart tube morphology'), ('MP:0011108', 'embryonic lethality during organogenesis, incomplete penetrance'), ('MP:0011483', 'renal glomerular synechia'), ('HP:0001967', None), ('MP:0001705', 'abnormal proximal-distal axis patterning'), ('MP:0001680', 'abnormal mesoderm development'), ('MP:0001318', 'pupil opacity'), ('MP:0001320', 'small pupils'), ('HP:0000238', None), ('HP:0000517', None), ('MP:0011869', 'detached podocyte'), ('MP:0005199', 'abnormal iris pigment epithelium'), ('HP:0000096', None), ('HP:0001513', None)]
MGI:3643265 Gm9143 []
MGI:96923 Mbl1 [('MP:0008497', 'decreased IgG2b level'), ('MP:0005387', 'immune system phenotype'), ('MP:0008502', 'increased IgG3 level'), ('HP:0010701', None), ('MP:0008554', 'decreased circulating tumor necrosis factor level'), ('MP:0009764', 'decreased sensitivity to induced morbidity/mortality')]
MGI:1929216 Ap3s1-ps1 []
MGI:1915650 0610043K17Rik []
MGI:5530764 Mir8114 []
MGI:3705130 Gm13166 []
MGI:3704354 Gm10275 []
MGI:1916846 Ap5s1 []
MGI:3650439 Amd-ps4 []
MGI:5477020 Gm26526 []
MGI:95453 Smarcad1 [('HP:0030040', None), ('MP:0004174', 'abnormal spine curvature'), ('HP:0100891', None), ('MP:0011088', 'neonatal lethality, incomplete penetrance'), ('HP:0009121', None), ('HP:0000902', None), ('MP:0004200', 'decreased fetal size'), ('MP:0008146', 'asymmetric sternocostal joints'), ('MP:0001625', 'cardiac hypertrophy'), ('MP:0009703', 'decreased birth body size'), ('MP:0000462', 'abnormal digestive system morphology'), ('MP:0002161', 'abnormal fertility/fecundity'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0001923', 'reduced female fertility')]
MGI:1919004 Cyp2d40 []
MGI:2442798 Dhrs9 []
MGI:2443063 Mylk3 [('MP:0003915', 'increased left ventricle weight'), ('HP:0001712', None), ('MP:0009763', 'increased sensitivity to induced morbidity/mortality'), ('MP:0005385', 'cardiovascular system phenotype'), ('MP:0012735', 'abnormal response to exercise'), ('MP:0010632', 'cardiac muscle necrosis'), ('MP:0010724', 'thick interventricular septum'), ('MP:0002833', 'increased heart weight'), ('HP:0001635', None), ('MP:0005608', 'cardiac interstitial fibrosis'), ('MP:0010754', 'abnormal heart left ventricle pressure')]
MGI:4834226 Mir3058 []
MGI:3836982 Mir1943 []
MGI:1306806 Cyp2c37 []
MGI:105388 Cdkn2c [('MP:0000693', 'spleen hyperplasia'), ('MP:0002083', 'premature death'), ('HP:0000803', None), ('HP:0012505', None), ('HP:0001251', None), ('MP:0002364', 'abnormal thymus size'), ('MP:0000702', 'enlarged lymph nodes'), ('HP:0040171', None), ('MP:0006262', 'increased testis tumor incidence'), ('MP:0002048', 'increased lung adenoma incidence'), ('MP:0002041', 'increased pituitary adenoma incidence'), ('MP:0010299', 'increased mammary gland tumor incidence'), ('HP:0010516', None), ('HP:0000053', None), ('HP:0004324', None), ('MP:0013602', 'abnormal Leydig cell differentiation'), ('MP:0000889', 'abnormal cerebellar molecular layer'), ('HP:0200058', None), ('MP:0002348', 'abnormal lymph node medulla morphology'), ('MP:0000630', 'mammary gland hyperplasia'), ('MP:0001264', 'increased body size'), ('HP:0005404', None), ('MP:0000523', 'cortical renal glomerulopathies')]
MGI:2448399 Hist1h2bk []
MGI:3651950 Gm12435 []
MGI:1351670 Hsd17b6 []
MGI:3781871 Gm3695 []
MGI:3649989 Rpl12-ps2 []
MGI:3648820 Gm7129 []
MGI:3651867 Gm13641 []
MGI:1914948 Fam174a []
MGI:3650794 Gm12271 []
MGI:1914742 Zfand4 []
MGI:2682935 Tmprss13 [('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0001240', 'abnormal epidermis stratum corneum morphology'), ('MP:0001282', 'short vibrissae'), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:1309467 Gstm6 []
MGI:3644053 Gm7676 []
MGI:97976 Rnu2-10 []
MGI:88328 Tnfsf8 [('GO:0045944PHENOTYPE', None), ('GO:0043374PHENOTYPE', None), ('MP:0005673', 'decreased susceptibility to graft versus host disease')]
MGI:1913470 Zdhhc12 []
MGI:3650076 Gm14400 []
MGI:5453858 Gm24081 []
MGI:1915690 Dusp23 []
MGI:1098773 Tmem141 []
MGI:3652187 Rps15a-ps4 []
MGI:95486 Fbl [('MP:0011101', 'prenatal lethality, incomplete penetrance'), ('MP:0001730', 'embryonic growth arrest'), ('MP:0011094', 'embryonic lethality before implantation, complete penetrance')]
MGI:5453488 Gm23711 []
MGI:2387617 Obp2a [('HP:0009887', None)]
MGI:3651166 Gm11843 []
MGI:1313291 Vezf1 [('HP:0100763', None), ('MP:0011090', 'perinatal lethality, incomplete penetrance'), ('MP:0005380', 'embryo phenotype'), ('MP:0002672', 'abnormal pharyngeal arch artery morphology'), ('MP:0000260', 'abnormal angiogenesis'), ('GO:0001885PHENOTYPE', None), ('MP:0011109', 'lethality throughout fetal growth and development, incomplete penetrance'), ('MP:0001698', 'decreased embryo size'), ('HP:0011029', None), ('HP:0002597', None), ('MP:0000265', 'atretic vasculature'), ('GO:0001525PHENOTYPE', None)]
MGI:3650537 Gm14165 []
MGI:98304 St3gal1 []
MGI:3648857 Gm6430 []
MGI:3704220 Gm9762 []
MGI:5456234 Gm26457 []
MGI:106025 Gucy2g [('MP:0005367', 'renal/urinary system phenotype')]
MGI:3780904 Gm2735 []
MGI:105112 Hpx [('GO:0051246PHENOTYPE', None), ('MP:0012666', 'increased circulating haptoglobin level'), ('GO:0020027PHENOTYPE', None)]
MGI:1888594 Icmt [('MP:0012235', 'abnormal liver bud morphology'), ('HP:0000980', None), ('GO:0008104PHENOTYPE', None), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0012504', 'increased forebrain apoptosis'), ('MP:0000596', 'abnormal liver development'), ('MP:0001265', 'decreased body size'), ('GO:0001701PHENOTYPE', None), ('MP:0000352', 'decreased cell proliferation')]
MGI:3780699 Gm2531 []
MGI:1915071 Atp1b4 []
MGI:1916078 Sync [('MP:0002841', 'impaired skeletal muscle contractility'), ('MP:0005369', 'muscle phenotype')]
MGI:5453021 Gm23244 []
MGI:1336893 Rnu12 []
MGI:2158650 Idh3b []
MGI:1929763 Cdc42ep1 []
MGI:1919007 Selenoo []
MGI:2181053 Dnajc28 [('MP:0011968', 'decreased threshold for auditory brainstem response'), ('MP:0002574', 'increased vertical activity'), ('MP:0002942', 'decreased circulating alanine transaminase level')]
MGI:2442106 Lrtm1 []
MGI:1916233 Rint1 [('HP:0001402', None), ('HP:0001028', None), ('MP:0002083', 'premature death'), ('MP:0013293', 'embryonic lethality prior to tooth bud stage'), ('MP:0003570', 'increased uterus leiomyoma incidence'), ('HP:0002904', None), ('MP:0002020', 'increased tumor incidence'), ('MP:0002702', 'decreased circulating free fatty acid level'), ('MP:0013488', 'increased keratoacanthoma incidence'), ('MP:0002014', 'increased papilloma incidence'), ('MP:0010299', 'increased mammary gland tumor incidence'), ('HP:0100615', None), ('MP:0011096', 'embryonic lethality between implantation and somite formation, complete penetrance'), ('HP:0006739', None)]
MGI:2684939 Fam170a []
MGI:3649320 Gm12912 []
MGI:1915265 Trap1 [('MP:0000693', 'spleen hyperplasia'), ('HP:0004325', None), ('HP:0001392', None), ('MP:0010959', 'abnormal oxidative phosphorylation'), ('MP:0003674', 'oxidative stress'), ('HP:0012648', None), ('MP:0002052', 'decreased tumor incidence')]
MGI:1858414 Ngef []
MGI:109330 Prop1 [('HP:0000135', None), ('MP:0002083', 'premature death'), ('MP:0008338', 'decreased thyrotroph cell number'), ('MP:0002270', 'abnormal pulmonary alveolus morphology'), ('GO:0009953PHENOTYPE', None), ('MP:0005130', 'decreased follicle stimulating hormone level'), ('HP:0030341', None), ('MP:0008330', 'absent somatotrophs'), ('HP:0012503', None), ('HP:0002098', None), ('MP:0005132', 'decreased luteinizing hormone level'), ('GO:0021979PHENOTYPE', None), ('MP:0008321', 'small adenohypophysis'), ('MP:0011088', 'neonatal lethality, incomplete penetrance'), ('HP:0001254', None), ('MP:0002777', 'absent ovarian follicles'), ('HP:0008222', None), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('GO:0048732PHENOTYPE', None), ('HP:0002795', None), ('MP:0003816', 'abnormal pituitary gland development'), ('HP:0100750', None), ('MP:0001265', 'decreased body size'), ('HP:0000961', None)]
MGI:1913656 Sac3d1 [('HP:0100494', None), ('MP:0002398', 'abnormal bone marrow cell morphology/development'), ('GO:0050776PHENOTYPE', None), ('HP:0010516', None), ('MP:0005348', 'increased T cell proliferation')]
MGI:108052 Bcl2l2 [('MP:0004996', 'abnormal CNS synapse formation'), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('MP:0008572', 'abnormal Purkinje cell dendrite morphology'), ('HP:0000027', None), ('MP:0004910', 'decreased seminal vesicle weight'), ('HP:0010791', None), ('MP:0020355', 'abnormal Sertoli cell barrier morphology'), ('HP:0012243', None), ('HP:0008322', None)]
MGI:3642960 Cyp2c54 []
MGI:3650756 Myadml2os []
MGI:104593 Xcl1 [('MP:0001844', 'autoimmune response'), ('MP:0001870', 'salivary gland inflammation'), ('MP:0008880', 'lacrimal gland inflammation'), ('MP:0005387', 'immune system phenotype'), ('MP:0005079', 'decreased cytotoxic T cell cytolysis'), ('HP:0002715', None), ('HP:0012649', None), ('HP:0012115', None)]
MGI:3704367 Gm10146 []
MGI:3039594 A2ml1 []
MGI:3643871 Gm6272 []
MGI:95480 Fancc [('HP:0000134', None), ('MP:0002209', 'decreased germ cell number'), ('MP:0008813', 'decreased common myeloid progenitor cell number'), ('MP:0002216', 'abnormal seminiferous tubule morphology'), ('MP:0000239', 'absent common myeloid progenitor cells'), ('MP:0001935', 'decreased litter size'), ('MP:0004045', 'abnormal cell cycle checkpoint function'), ('MP:0004030', 'induced chromosome breakage'), ('MP:0001545', 'abnormal hematopoietic system physiology'), ('MP:0001154', 'seminiferous tubule degeneration'), ('HP:0000013', None), ('MP:0001921', 'reduced fertility'), ('MP:0008249', 'abnormal common lymphocyte progenitor cell morphology'), ('GO:0002262PHENOTYPE', None), ('HP:0000798', None), ('MP:0004029', 'spontaneous chromosome breakage'), ('HP:0008724', None), ('HP:0001873', None), ('GO:0007276PHENOTYPE', None), ('MP:0001155', 'arrest of spermatogenesis'), ('MP:0001923', 'reduced female fertility')]
MGI:3646825 Rpl21-ps4 []
MGI:5313103 Gm20656 []
MGI:1924792 Nxpe4 []
MGI:96761 Ldha-ps2 []
MGI:3647791 Gm5257 []
MGI:3779623 Gm6710 []
MGI:1861755 Nsmf [('MP:0008335', 'decreased gonadotroph cell number'), ('MP:0001922', 'reduced male fertility'), ('HP:0000823', None), ('MP:0005561', 'increased mean corpuscular hemoglobin'), ('MP:0001923', 'reduced female fertility')]
MGI:1354956 Tfr2 [('MP:0008955', 'increased cellular hemoglobin content'), ('HP:0004325', None), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0011890', 'increased circulating ferritin level'), ('MP:0008810', 'increased circulating iron level'), ('HP:0011031', None), ('MP:0008809', 'increased spleen iron level'), ('MP:0008808', 'decreased spleen iron level'), ('MP:0005638', 'hemochromatosis')]
MGI:95928 H2-Q1 []
MGI:2144518 Stac2 []
MGI:3801854 Gm16005 []
MGI:2183434 Wfdc12 []
MGI:88082 Asgr2 [('MP:0009763', 'increased sensitivity to induced morbidity/mortality'), ('MP:0005376', 'homeostasis/metabolism phenotype')]
MGI:1196295 Ptpn20 [('MP:0001513', 'limb grasping'), ('HP:0008887', None)]
MGI:2180781 Slc8b1 [('MP:0001523', 'impaired righting response'), ('MP:0005560', 'decreased circulating glucose level')]
MGI:1916083 Pdcl3 []
MGI:1924165 Mnd1 [('HP:0008669', None)]
MGI:1927498 Rgs18 [('MP:0002398', 'abnormal bone marrow cell morphology/development'), ('MP:0005386', 'behavior/neurological phenotype'), ('HP:0001873', None)]
MGI:2143558 Chchd10 []
MGI:4936885 Gm17251 []
MGI:3648679 Rpl10-ps2 []
MGI:5452422 Gm22645 []
MGI:1914215 Ctu2 []
MGI:4937303 Gm17669 []
MGI:3781253 Gm3076 []
MGI:1924750 Ppfia1 []
MGI:2675256 Adm2 []
MGI:2445361 Serpinb1b []
MGI:3781646 Gm3470 []
MGI:3782365 Gm4189 []
MGI:3705106 Gm15318 []
MGI:1913881 Mfsd14b []
MGI:1925947 Pus7 []
MGI:1924117 Rhbdd1 [('MP:0002896', 'abnormal bone mineralization')]
MGI:5453405 Gm23628 []
MGI:4938023 Gm17196 []
MGI:102896 Sult1a1 [('MP:0009642', 'abnormal blood homeostasis')]
MGI:2146071 Tubgcp6 []
MGI:2443387 Zc3hav1l []
MGI:3713585 Zfp993 []
MGI:3783212 Gm15770 []
MGI:1928486 Tdo2 [('MP:0005332', 'abnormal amino acid level'), ('MP:0004948', 'abnormal neuronal precursor proliferation'), ('MP:0013908', 'small lateral ventricles')]
MGI:3648782 Rpl26-ps2 []
MGI:3649297 Gm13140 []
MGI:3704332 Gm9830 []
MGI:1096381 Arntl [('MP:0002563', 'shortened circadian period'), ('MP:0002607', 'decreased basophil cell number'), ('MP:0002083', 'premature death'), ('MP:0005595', 'abnormal vascular smooth muscle physiology'), ('MP:0005489', 'vascular smooth muscle hyperplasia'), ('GO:0007623PHENOTYPE', None), ('HP:0100240', None), ('MP:0005664', 'decreased circulating noradrenaline level'), ('HP:0011014', None), ('MP:0003059', 'decreased insulin secretion'), ('HP:0001888', None), ('MP:0009168', 'decreased pancreatic islet number'), ('MP:0002560', 'arrhythmic circadian persistence'), ('MP:0008190', 'decreased transitional stage B cell number'), ('MP:0005370', 'liver/biliary system phenotype'), ('HP:0001288', None), ('MP:0002891', 'increased insulin sensitivity'), ('MP:0005591', 'decreased vasodilation'), ('HP:0001944', None), ('MP:0002907', 'abnormal parturition'), ('MP:0020332', 'impaired leukocyte tethering or rolling'), ('MP:0008908', 'increased total fat pad weight'), ('MP:0004953', 'decreased spleen weight'), ('HP:0012184', None), ('MP:0005185', 'decreased circulating progesterone level'), ('MP:0020408', 'altered susceptibility to induced thrombosis'), ('HP:0005645', None), ('MP:0005369', 'muscle phenotype'), ('HP:0010976', None), ('MP:0003976', 'decreased circulating VLDL triglyceride level'), ('MP:0005631', 'decreased lung weight'), ('HP:0003138', None), ('MP:0002840', 'abnormal lens fiber morphology'), ('MP:0003918', 'decreased kidney weight'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0004876', 'decreased mean systemic arterial blood pressure'), ('MP:0010686', 'abnormal hair follicle matrix region morphology'), ('HP:0001649', None), ('MP:0008858', 'abnormal hair cycle anagen phase'), ('MP:0009133', 'decreased white fat cell size'), ('MP:0004988', 'increased osteoblast cell number'), ('HP:0001662', None), ('MP:0003198', 'calcified tendon'), ('MP:0001728', 'failure of embryo implantation'), ('MP:0001392', 'abnormal locomotor behavior'), ('MP:0001391', 'abnormal tail movements'), ('HP:0012311', None), ('MP:0002941', 'increased circulating alanine transaminase level'), ('HP:0100827', None), ('MP:0001792', 'impaired wound healing'), ('HP:0008222', None), ('MP:0005048', 'abnormal thrombosis'), ('MP:0010213', 'abnormal circulating fibrinogen level'), ('HP:0000833', None), ('MP:0002834', 'decreased heart weight'), ('MP:0004905', 'decreased uterus weight'), ('MP:0005397', 'hematopoietic system phenotype'), ('MP:0004232', 'decreased muscle weight'), ('MP:0005551', 'abnormal eye electrophysiology'), ('MP:0005386', 'behavior/neurological phenotype'), ('MP:0005317', 'increased triglyceride level'), ('MP:0005300', 'abnormal corneal stroma morphology'), ('HP:0003124', None), ('MP:0000427', 'abnormal hair cycle'), ('HP:0001367', None), ('MP:0004852', 'decreased testis weight'), ('HP:0002533', None), ('HP:0000481', None), ('MP:0005504', 'abnormal ligament morphology')]
MGI:3802146 Gm16023 []
MGI:3649580 Rpl38-ps1 []
MGI:1920257 Rnf169 [('HP:0000938', None), ('HP:0012311', None)]
MGI:5521015 Obox4-ps2 []
MGI:109269 Inhbe []
MGI:3645637 Gm14794 []
MGI:1332226 Soat2 [('MP:0003983', 'decreased cholesterol level'), ('HP:0012184', None), ('MP:0003982', 'increased cholesterol level'), ('MP:0002310', 'decreased susceptibility to hepatic steatosis'), ('MP:0009356', 'decreased liver triglyceride level'), ('MP:0004773', 'abnormal bile composition'), ('HP:0003146', None)]
MGI:5477367 Gm26873 []
MGI:4414958 Gm16538 []
MGI:3648402 Gm15204 []
MGI:2183691 Nav2 [('MP:0001489', 'decreased startle reflex'), ('GO:0021554PHENOTYPE', None), ('GO:0007605PHENOTYPE', None), ('MP:0001973', 'increased thermal nociceptive threshold'), ('HP:0001824', None), ('HP:0004408', None)]
MGI:4937960 Gm17133 []
MGI:107304 Rny3 []
MGI:3652179 Gm13267 []
MGI:2682302 Zbed4 [('MP:0011110', 'preweaning lethality, incomplete penetrance'), ('HP:0001627', None), ('HP:0000105', None), ('HP:0001640', None), ('HP:0001744', None), ('HP:0001743', None)]
MGI:2676901 Mir26b []
MGI:104767 Gpx4 [('HP:0004326', None), ('MP:0009230', 'abnormal sperm head morphology'), ('MP:0008267', 'abnormal hippocampus CA3 region morphology'), ('MP:0001674', 'abnormal germ layer development'), ('MP:0000859', 'abnormal somatosensory cortex morphology'), ('HP:0003251', None), ('MP:0001935', 'decreased litter size'), ('HP:0001254', None), ('MP:0005384', 'cellular phenotype'), ('MP:0009836', 'abnormal sperm principal piece morphology'), ('MP:0000947', 'convulsive seizures'), ('HP:0012206', None), ('MP:0002086', 'abnormal extraembryonic tissue morphology'), ('MP:0004543', 'abnormal sperm physiology'), ('MP:0001648', 'abnormal apoptosis'), ('HP:0002446', None), ('HP:0000735', None), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0005389', 'reproductive system phenotype'), ('MP:0009237', 'kinked sperm flagellum')]
MGI:1913357 Wfdc21 []
MGI:5454785 Gm25008 []
MGI:97991 Rnu6-ps2 []
MGI:1921692 Sgms2 [('MP:0009289', 'decreased epididymal fat pad weight'), ('MP:0010080', 'abnormal hepatocyte physiology'), ('MP:0001547', 'abnormal lipid level')]
MGI:3642955 Gm5643 []
MGI:4421973 n-R5s121 []
MGI:105387 Cdkn2d [('HP:0000029', None), ('HP:0008232', None), ('GO:0007605PHENOTYPE', None), ('MP:0004398', 'cochlear inner hair cell degeneration')]
MGI:5011755 Mroh6 []
MGI:2183102 Sardh []
MGI:1915781 1110020A21Rik []
MGI:88331 Cd3d [('HP:0005415', None), ('MP:0001825', 'arrested T cell differentiation')]
MGI:98511 Tfe3 [('MP:0002169', 'no abnormal phenotype detected')]
MGI:3819494 Snora23 []
MGI:2448407 Hist1h2bn []
MGI:3708125 Gm15191 []
MGI:3651881 Gm11246 []
MGI:4439832 Carmn []
MGI:107364 Il17a [('HP:0005479', None), ('MP:0000322', 'increased granulocyte number'), ('MP:0008348', 'absent gamma-delta T cells'), ('HP:0002090', None), ('MP:0008497', 'decreased IgG2b level'), ('HP:0011990', None), ('MP:0008075', 'decreased CD4-positive, alpha beta T cell number'), ('HP:0002850', None), ('HP:0004313', None), ('MP:0005562', 'decreased mean corpuscular hemoglobin'), ('HP:0001508', None), ('MP:0005362', 'abnormal Langerhans cell physiology'), ('MP:0001179', 'thick pulmonary interalveolar septum'), ('MP:0008495', 'decreased IgG1 level'), ('HP:0004315', None), ('MP:0002267', 'abnormal bronchiole morphology')]
MGI:3650315 Gm11951 []
MGI:1913344 Lsm7 []
MGI:1891066 Aqp9 []
MGI:3648907 Gm7666 []
MGI:3704336 Rpl10-ps3 []
MGI:105304 Il6ra [('MP:0005343', 'increased circulating aspartate transaminase level'), ('MP:0008553', 'increased circulating tumor necrosis factor level'), ('MP:0005376', 'homeostasis/metabolism phenotype'), ('MP:0003631', 'nervous system phenotype'), ('MP:0004185', 'abnormal adipocyte glucose uptake'), ('MP:0010214', 'abnormal circulating serum amyloid protein level'), ('MP:0005026', 'decreased susceptibility to parasitic infection'), ('MP:0010751', 'decreased susceptibility to parasitic infection induced morbidity/mortality'), ('MP:0003887', 'increased hepatocyte apoptosis'), ('MP:0004001', 'decreased hepatocyte proliferation'), ('MP:0010398', 'decreased liver glycogen level'), ('HP:0002457', None), ('MP:0005463', 'abnormal CD4-positive, alpha-beta T cell physiology'), ('MP:0005375', 'adipose tissue phenotype'), ('MP:0004502', 'decreased incidence of tumors by chemical induction'), ('MP:0008596', 'increased circulating interleukin-6 level'), ('MP:0005023', 'abnormal wound healing'), ('MP:0005378', 'growth/size/body region phenotype'), ('MP:0002169', 'no abnormal phenotype detected')]
MGI:98270 Sds []
MGI:3644479 Hnrnpa1l2-ps []
MGI:2136459 Cdc42bpb []
MGI:1915065 Sec14l2 [('HP:0003146', None)]
MGI:105490 Gramd1a []
MGI:1927343 Rps6kb2 [('MP:0002169', 'no abnormal phenotype detected')]
MGI:1921402 Stra6l []
MGI:4937268 Gm17634 []
MGI:3782538 Gm4353 []
MGI:97855 Rap2a []
MGI:3647418 Cfhr3 []
MGI:3819492 Snora20 []
MGI:3782792 Gm4609 []
MGI:5530911 Gm27529 []
MGI:1916196 1500002C15Rik []
MGI:894332 Ebf2 [('HP:0003330', None), ('MP:0002741', 'small olfactory bulb'), ('MP:0008789', 'abnormal olfactory epithelium morphology'), ('MP:0001940', 'testis hypoplasia'), ('HP:0000035', None), ('HP:0007033', None), ('MP:0000852', 'small cerebellum'), ('MP:0008152', 'decreased diameter of femur'), ('HP:0012243', None), ('HP:0011314', None), ('HP:0100671', None), ('MP:0012014', 'abnormal olfactory neuron innervation pattern'), ('MP:0002566', 'abnormal sexual interaction'), ('MP:0005236', 'abnormal olfactory nerve morphology'), ('MP:0002161', 'abnormal fertility/fecundity'), ('MP:0002651', 'abnormal sciatic nerve morphology'), ('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('HP:0001508', None), ('HP:0000762', None), ('MP:0003631', 'nervous system phenotype'), ('MP:0002631', 'abnormal epididymis morphology'), ('HP:0012286', None)]
MGI:3648626 Gm5417 []
MGI:1920933 Atg16l2 []
MGI:3646307 Gbp11 []
MGI:3801721 Gm16157 []
MGI:3643179 Gm5537 []
MGI:98283 Srsf1 [('MP:0002083', 'premature death'), ('MP:0000280', 'thin ventricular wall'), ('MP:0008106', 'decreased amacrine cell number'), ('HP:0001093', None), ('MP:0010235', 'abnormal retina inner limiting membrane morphology'), ('GO:0001701PHENOTYPE', None), ('MP:0005241', 'abnormal retinal ganglion layer morphology'), ('HP:0000546', None)]
MGI:3648597 Gm9095 []
MGI:3645191 Gm8355 []
MGI:3646640 Rplp1-ps1 []
MGI:1919202 Riox1 [('MP:0004985', 'decreased osteoclast cell number'), ('MP:0004988', 'increased osteoblast cell number'), ('MP:0009673', 'increased birth weight'), ('HP:0012790', None), ('MP:0008272', 'abnormal endochondral bone ossification'), ('MP:0003408', 'increased width of hypertrophic chondrocyte zone')]
MGI:1345963 Coro1b [('MP:0005387', 'immune system phenotype')]
MGI:5455250 Gm25473 []
MGI:3648695 Hmgb1-ps7 []
MGI:1098808 Pex5 [('MP:0002083', 'premature death'), ('MP:0000478', 'delayed intestine development'), ('MP:0001154', 'seminiferous tubule degeneration'), ('MP:0005370', 'liver/biliary system phenotype'), ('HP:0011014', None), ('HP:0001251', None), ('MP:0011085', 'postnatal lethality, complete penetrance'), ('HP:0002098', None), ('MP:0010956', 'abnormal mitochondrial ATP synthesis coupled electron transport'), ('MP:0009642', 'abnormal blood homeostasis'), ('HP:0012087', None), ('MP:0010952', 'abnormal fatty acid beta-oxidation'), ('HP:0002808', None), ('HP:0012647', None), ('MP:0010955', 'abnormal respiratory electron transport chain'), ('HP:0001392', None), ('MP:0001889', 'delayed brain development'), ('MP:0008019', 'increased liver tumor incidence'), ('HP:0002240', None), ('HP:0001324', None), ('MP:0003984', 'embryonic growth retardation'), ('MP:0008489', 'slow postnatal weight gain'), ('MP:0000754', 'paresis'), ('MP:0000528', 'delayed kidney development'), ('MP:0008026', 'abnormal brain white matter morphology'), ('MP:0004852', 'decreased testis weight')]
MGI:1919332 Cyp2c55 []
MGI:3646410 Ifi206 []
MGI:3826519 Gm16305 []
MGI:5452357 Gm22580 []
MGI:3646863 Gm8129 []
MGI:2664996 Tpt1-ps2 []
MGI:4422076 n-R5s211 []
MGI:5453074 Gm23297 []
MGI:5454980 Gm25203 []
MGI:2136910 Hemgn [('MP:0011968', 'decreased threshold for auditory brainstem response')]
MGI:1922484 Rnf19b [('MP:0008567', 'decreased interferon-gamma secretion'), ('MP:0001272', 'increased metastatic potential'), ('GO:0042267PHENOTYPE', None), ('GO:0072643PHENOTYPE', None)]
MGI:3644144 Gm8034 []
MGI:1345193 Nlrp5 [('MP:0003718', 'maternal effect'), ('GO:0043623PHENOTYPE', None), ('HP:0008222', None), ('GO:0043487PHENOTYPE', None), ('GO:0034613PHENOTYPE', None)]
MGI:2443051 Egfros []
MGI:1921160 Arhgap18 [('MP:0001257', 'increased body length'), ('HP:0003072', None)]
MGI:3647121 Gm5611 []
MGI:3646686 Gm5909 []
MGI:5547772 Gm28036 []
MGI:3649750 Hspd1-ps4 []
MGI:2156391 Pramel7 []
MGI:3781826 Gm3650 []
MGI:1351596 Sh2d2a [('MP:0008567', 'decreased interferon-gamma secretion'), ('MP:0008699', 'increased interleukin-4 secretion'), ('GO:0008283PHENOTYPE', None)]
MGI:3783235 Gm15793 []
MGI:1351326 Nrk [('MP:0011086', 'postnatal lethality, incomplete penetrance'), ('MP:0012098', 'increased spongiotrophoblast size'), ('MP:0011088', 'neonatal lethality, incomplete penetrance'), ('GO:0008285PHENOTYPE', None), ('HP:0006267', None)]
MGI:4415001 Gm16581 []
MGI:1859609 Sfmbt1 []
MGI:3649822 Gm12344 []
MGI:2442750 Slc22a30 []
MGI:1349410 Triobp [('MP:0011967', 'increased or absent threshold for auditory brainstem response'), ('MP:0008762', 'embryonic lethality'), ('HP:0000365', None), ('MP:0004523', 'decreased cochlear hair cell stereocilia number')]
MGI:1347476 Foxa2 [('MP:0001394', 'circling'), ('MP:0009177', 'decreased pancreatic alpha cell number'), ('MP:0002082', 'postnatal lethality'), ('GO:0090009PHENOTYPE', None), ('MP:0010861', 'increased respiratory mucosa goblet cell number'), ('HP:0011014', None), ('MP:0010935', 'increased airway resistance'), ('MP:0008029', 'abnormal paraxial mesoderm morphology'), ('HP:0030781', None), ('MP:0009331', 'absent primitive node'), ('MP:0011939', 'increased food intake'), ('MP:0000313', 'abnormal cell death'), ('MP:0003935', 'abnormal craniofacial development'), ('MP:0011085', 'postnatal lethality, complete penetrance'), ('MP:0012082', 'delayed heart development'), ('MP:0002085', 'abnormal embryonic tissue morphology'), ('MP:0000926', 'absent floor plate'), ('MP:0011098', 'embryonic lethality during organogenesis, complete penetrance'), ('MP:0010903', 'abnormal pulmonary alveolus wall morphology'), ('MP:0006027', 'impaired lung alveolus development'), ('HP:0002155', None), ('MP:0005440', 'increased glycogen level'), ('MP:0013504', 'increased embryonic tissue cell apoptosis'), ('MP:0001505', 'hunched posture'), ('MP:0011092', 'embryonic lethality, complete penetrance'), ('HP:0001250', None), ('MP:0002696', 'decreased circulating glucagon level'), ('GO:0008344PHENOTYPE', None), ('MP:0005387', 'immune system phenotype'), ('MP:0011732', 'decreased somite size'), ('MP:0011088', 'neonatal lethality, incomplete penetrance'), ('MP:0010896', 'decreased lung compliance'), ('MP:0002275', 'abnormal type II pneumocyte morphology'), ('HP:0100547', None), ('MP:0001698', 'decreased embryo size'), ('MP:0005560', 'decreased circulating glucose level'), ('HP:0001939', None), ('GO:0032525PHENOTYPE', None), ('MP:0010856', 'dilated respiratory conducting tubes'), ('MP:0011733', 'fused somites'), ('MP:0002169', 'no abnormal phenotype detected'), ('MP:0001385', 'pup cannibalization'), ('MP:0001685', 'abnormal endoderm development'), ('MP:0003960', 'increased lean body mass'), ('GO:0071542PHENOTYPE', None), ('MP:0005217', 'abnormal pancreatic beta cell morphology'), ('MP:0003400', 'kinked neural tube'), ('MP:0003861', 'abnormal nervous system development'), ('MP:0002314', 'abnormal respiratory mechanics'), ('MP:0001691', 'abnormal somite shape'), ('MP:0009937', 'abnormal neuron differentiation'), ('MP:0001265', 'decreased body size'), ('MP:0001688', 'abnormal somite development'), ('MP:0011183', 'abnormal primitive endoderm morphology'), ('HP:0011063', None)]
MGI:4421949 n-R5s101 []
MGI:1861380 Sphk2 [('HP:0002846', None), ('MP:0005463', 'abnormal CD4-positive, alpha-beta T cell physiology')]
MGI:1919258 Zfyve19 []
MGI:5521100 Gtpbp4-ps1 []
| BSD-3-Clause | notebooks/Gene_Expression.ipynb | alliance-genome/ontobio |
OverviewAs data scientists working in a cyber-security company, we wanted to show that Natural Language Processing (NLP) algorithms can be applied to security related events. For this task we used 2 algorithm developed by Google: **Word2vec** ([link](https://arxiv.org/abs/1301.3781)) and **Doc2vec** ([link](https://arxiv.org/abs/1405.4053)). These algorithms use the context of words to extract a vectorized representation (aka embedding) for each word/document in a given vocabulary. If you want to learn about how **Word2vec** works, you can [start here](https://skymind.ai/wiki/word2vec).Using these algorithms, we managed to model the behavior of common vulnerability scanners (and other client applications) based on their unique 'syntax' of malicious web requests. We named our implementation **Mal2vec**. About this notebookThis notebook contains easy to use widgets to execute each step on your own data. We also include 3 datasets as examples of how to use this project. Table of contents- [Load csv data file](Load-CSV-data-file)- [Map columns](Map-columns)- [Select additional grouping columns](Select-additional-grouping-columns)- [Create sentences](Create-sentences)- [Prepare dataset](Prepare-dataset)- [Train classification model](Train-classifictaion-model)- [Evaluate the model](Evaluate-the-model) Imports | import random
from IPython.display import display, Markdown, clear_output, HTML
def hide_toggle():
# @author: harshil
# @Source: https://stackoverflow.com/a/28073228/6306692
this_cell = """$('div.cell.code_cell.rendered.selected')"""
next_cell = this_cell + '.next()'
toggle_text = 'Show/hide code' # text shown on toggle link
target_cell = this_cell # target cell to control with toggle
js_hide_current = '' # bit of JS to permanently hide code in current cell (only when toggling next cell)
js_f_name = 'code_toggle_{}'.format(str(random.randint(1,2**64)))
html = """
<script>
function {f_name}() {{
{cell_selector}.find('div.input').toggle();
}}
{js_hide_current}
</script>
<a href="javascript:{f_name}()">{toggle_text}</a>
""".format(
f_name=js_f_name,
cell_selector=target_cell,
js_hide_current=js_hide_current,
toggle_text=toggle_text
)
return HTML(html)
display(hide_toggle())
display(HTML('''<style>.text_cell {background: #E0E5EE;}
.widget-inline-hbox .widget-label{width:120px;}</style>'''))
%load_ext autoreload
%autoreload 2
import os
import pandas as pd
import ipywidgets as widgets
import sys
sys.path.append("..")
from classify import prepare_dataset, train_classifier
from vizualize import draw_model, plot_model_results
from sentensize import create_sentences, dump_sentences | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Load CSV data file Ready to use dataset - Customer Complaints- Open source dataset by U.S. gov ([link](https://catalog.data.gov/dataset/consumer-complaint-database))- **Events**: the first word in the column 'issue' - **Label**: the product- **Groupping by**: 'Zip code' | display(hide_toggle())
df = None
def load_csv(btn):
global df
clear_output()
display(hide_toggle())
display(widgets.VBox([filename_input, nrows_input]))
display(HTML('<img src="../loading.gif" alt="Drawing" style="width: 50px;"/>'))
nrows = int(nrows_input.value)
df = pd.read_csv(filename_input.value, nrows=nrows if nrows > 0 else None)
clear_output()
display(hide_toggle())
display(widgets.VBox([filename_input, nrows_input, load_button]))
print('Loaded {} rows'.format(df.shape[0]))
display(df.sample(n=5))
filename_input = widgets.Text(description='CSV file:', value='data/complaints.gz')
nrows_input = widgets.Text(description='Rows limit:', value='0')
load_button = widgets.Button(description='Load CSV')
load_button.on_click(load_csv)
widgets.VBox([filename_input, nrows_input, load_button]) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Map columnsThe data should have at least 3 columns:- **Timestamp** (int) - if you don't have timestamps, it can also be a simple increasing index- **Event** (string) - rule name, event description, etc. Must be a single word containing only alpha-numeric characters- **Label** (string) - type of event. This will be later used to create the classification model | time_column_input, event_column_input, label_column_input = None, None, None
def show_dropdown(obj):
global time_column_input, event_column_input, label_column_input
time_column_input = widgets.Dropdown(options=df.columns, description='Time column:')
event_column_input = widgets.Dropdown(options=df.columns, value='Issue', description='Event column:')
label_column_input = widgets.Dropdown(options=df.columns, value='Product', description='Label column:')
clear_output()
display(hide_toggle())
display(widgets.VBox([show_dropdown_button, time_column_input, event_column_input, label_column_input]))
show_dropdown_button = widgets.Button(description='Refresh')
show_dropdown_button.on_click(show_dropdown)
show_dropdown(None) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Select additional grouping columnsSelect those columns which represents unique sequences | checkboxes = None
def show_checkboxes(obj):
global checkboxes
checkboxes = {k:widgets.Checkbox(description=k) for k in df.columns if k not in [time_column_input.value,
event_column_input.value,
label_column_input.value
]}
checkboxes['ZIP code'].value = True
clear_output()
display(hide_toggle())
display(widgets.VBox([show_checkboxes_button] + [checkboxes[x] for x in checkboxes]))
show_checkboxes_button = widgets.Button(description='Refresh')
show_checkboxes_button.on_click(show_checkboxes)
show_checkboxes(None) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Create sentencesThis cell will group events into sentences (using the grouping columns selected). It will then split sentences if to consecutive events are separated by more than the given timeout (default: 300 seconds) | display(hide_toggle())
dataset_name = os.path.splitext(os.path.basename(filename_input.value))[0]
sentences_df, sentences_filepath = None, None
def sentences(obj):
global sentences_df, sentences_filepath
clear_output()
display(hide_toggle())
display(HTML('<img src="../loading.gif" alt="Drawing" style="width: 50px;"/>'))
groupping_columns = [x for x in checkboxes if checkboxes[x].value]
sentences_df = create_sentences(df,
time_column_input.value,
event_column_input.value,
label_column_input.value,
groupping_columns,
timeout=300
)
sentences_filepath = dump_sentences(sentences_df, dataset_name)
clear_output()
display(hide_toggle())
display(sentence_button)
print('Created {} sentences. Showing 5 examples:'.format(sentences_df.shape[0]))
display(sentences_df.sample(n=5))
sentence_button = widgets.Button(description='Start')
display(sentence_button)
sentence_button.on_click(sentences) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Prepare dataset1) Train a doc2vec model to extract the embedding vector from each sentence. **Parameters**: *vector_size*: the size of embedding vector. Increasing this parameters might improve accuracy, but will take longer to train (int, default=30) *epochs*: how many epochs should be applied during training. Increasing this parameters might improve accuracy, but will take longer to train (int, default=50) *min_sentence_count*: don't classify labels with small amount of sentences (int, default=200) 2) Prepare dataset- Infer the embedding vector for each sample in the data set- Perform [stratified sampling](https://en.wikipedia.org/wiki/Stratified_sampling) for each label- Split to train/test sets 80%-20% | display(hide_toggle())
X_train, X_test, y_train, y_test, classes = None, None, None, None, None
def dataset(obj):
global sentences_df, sentences_filepath, dataset_name, X_train, X_test, y_train, y_test, classes
clear_output()
display(hide_toggle())
display(HTML('<img src="../loading.gif" alt="Drawing" style="width: 50px;"/>'))
X_train, X_test, y_train, y_test, classes = prepare_dataset(sentences_df,
sentences_filepath,
dataset_name,
vector_size=30,
epochs=50,
min_sentence_count=200
)
dataset_button.description = 'Run Again'
clear_output()
display(hide_toggle())
print('Dataset ready!')
display(dataset_button)
dataset_button = widgets.Button(description='Start')
display(dataset_button)
dataset_button.on_click(dataset) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Train classification modelTrain a deep neural network to classify each sentence to its correct label for 500 epochs (automatically stop when training no longer improves results)For the purpose of this demo, the network architecture and hyper-parameters are constant. Feel free the modify to code and improve the model | display(hide_toggle())
history, report, df_cm = None, None, None
def train(obj):
global dataset_name, X_train, X_test, y_train, y_test, classes, history, report, df_cm
train_button.description = 'Train Again'
clear_output()
display(hide_toggle())
display(train_button)
history, report, df_cm = train_classifier(X_train, X_test, y_train, y_test, classes, dataset_name)
train_button = widgets.Button(description='Start')
display(train_button)
train_button.on_click(train) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
Evaluate the modelPlot the results of the model:- **Loss** - how did the model progress during training (lower values mean better performance)- **Accuracy** - how did the model perform on the validation set (higher values are better)- **Confusion Matrix** - mapping each of the model's predictions (x-axis) to its true label (y-axis). Correct predictions are placed on the main diagonal (brighter is better)- **Detailed report** - for each label, show the following metrics: precision, recall, f1-score ([read more here](https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9)). The 'support' metric is the number of instances in that class | display(hide_toggle())
def evaluate(btn):
global history, report, df_cm
clear_output()
evaluate_button.description = 'Refresh'
display(hide_toggle())
display(evaluate_button)
plot_model_results(history, report, df_cm, classes)
evaluate_button = widgets.Button(description='Evaluate Model')
display(evaluate_button)
evaluate_button.on_click(evaluate) | _____no_output_____ | MIT | examples/complaints example.ipynb | imperva/mal2vec |
pyGSM (Python + GSM) pyGSM uses the powerful tools of python to allow for rapid prototyping and improved readability.* Reduction in number of lines ~12,000 vs ~30,000 and individual file size * Highly object oriented * Easy to read/use/prototype. No compiling!* No loss in performance since it uses high-performing numerical libraries * Interactive coding | import sys
sys.path.insert(0,'/home/caldaz/module/pyGSM')
from molecule import Molecule
from pes import PES
from avg_pes import Avg_PES
import numpy as np
from nifty import pvec1d,pmat2d
import matplotlib
import matplotlib.pyplot as plt
from pytc import *
import manage_xyz
from rhf_lot import *
from psiw import *
from nifty import getAllCoords,getAtomicSymbols,click,printcool
import pybel as pb
%matplotlib inline | _____no_output_____ | MIT | examples/ipynb_demos/pytc/.ipynb_checkpoints/Basic_API-checkpoint.ipynb | espottesmith/pyGSM |
1. Building the pyTC objects | printcool("Build resources")
resources = ls.ResourceList.build()
printcool('{}'.format(resources))
printcool("build the Lightspeed (pyTC) objecs")
filepath='data/ethylene.xyz'
molecule = ls.Molecule.from_xyz_file(filepath)
geom = geometry.Geometry.build(
resources=resources,
molecule=molecule,
basisname='6-31gs',
)
printcool('{}'.format(geom))
ref = RHF.from_options(
geometry= geom,
g_convergence=1.0E-6,
fomo=True,
fomo_method='gaussian',
fomo_temp=0.3,
fomo_nocc=7,
fomo_nact=2,
print_level=1,
)
ref.compute_energy()
casci = CASCI.from_options(
reference=ref,
nocc=7,
nact=2,
nalpha=1,
nbeta=1,
S_inds=[0],
S_nstates=[2],
print_level=1,
)
casci.compute_energy()
psiw = CASCI_LOT.from_options(
casci=casci,
rhf_guess=True,
rhf_mom=True,
orbital_coincidence='core',
state_coincidence='full',
) | #========================================================#
#| [92m build the Lightspeed (pyTC) objecs [0m |#
#========================================================#
#========================================================#
#| [92m Geometry: [0m |#
#| [92m QMMM = False [0m |#
#| [92m -D3 = False [0m |#
#| [92m ECP = False [0m |#
#| [92m [0m |#
#| [92m Molecule: ethylene [0m |#
#| [92m Natom = 6 [0m |#
#| [92m Charge = 0.000 [0m |#
#| [92m Multiplicity = 1.000 [0m |#
#| [92m [0m |#
#| [92m Basis: 6-31gs [0m |#
#| [92m nao = 38 [0m |#
#| [92m ncart = 38 [0m |#
#| [92m nprim = 46 [0m |#
#| [92m nshell = 20 [0m |#
#| [92m natom = 6 [0m |#
#| [92m pure? = No [0m |#
#| [92m max L = 2 [0m |#
#| [92m [0m |#
#| [92m Basis: cc-pvdz-minao [0m |#
#| [92m nao = 14 [0m |#
#| [92m ncart = 14 [0m |#
#| [92m nprim = 60 [0m |#
#| [92m nshell = 10 [0m |#
#| [92m natom = 6 [0m |#
#| [92m pure? = Yes [0m |#
#| [92m max L = 1 [0m |#
#| [92m [0m |#
#========================================================#
==> RHF <==
External Environment:
Enuc = 3.3333038401617195E+01
Eext = 3.3333038401617195E+01
SCF Iterations:
Iter: Energy dE dG Time[s]
0: -7.7092708661464698E+01 -7.709E+01 1.143E+00 0.841
1: -7.8147981534974207E+01 -1.055E+00 6.345E-02 0.050
2: -7.8169965996087384E+01 -2.198E-02 1.559E-02 0.050
3: -7.8172283892637140E+01 -2.318E-03 7.279E-03 0.049
4: -7.8172545611657412E+01 -2.617E-04 4.307E-04 0.048
5: -7.8172547344654049E+01 -1.733E-06 3.867E-05 0.046
6: -7.8172547366554227E+01 -2.190E-08 7.338E-06 0.041
7: -7.8172547366986933E+01 -4.327E-10 1.649E-06 0.037
8: -7.8172547366784329E+01 2.026E-10 1.337E-07 0.047
SCF Converged
SCF Energy = -7.8172547366784329E+01
SCF Internal Energy (E) = -7.7792973600257000E+01
SCF Entropy Term (-T * S) = -3.7957376652733060E-01
SCF Free Energy (E - T * S) = -7.8172547366784329E+01
==> End RHF <==
==> CASCI <==
External Environment:
Enuc = 3.3333038401617195E+01
Eext = 3.3333038401617195E+01
Core Energy = -76.85572592289878
=> S=0 States <=
CASCI S=0 Energies:
I: Total E
0: -7.8050956144574869E+01
1: -7.7668325171082003E+01
=> End S=0 States <=
==> End CASCI <==
| MIT | examples/ipynb_demos/pytc/.ipynb_checkpoints/Basic_API-checkpoint.ipynb | espottesmith/pyGSM |
Section 2: Building the pyGSM Objects | printcool("Build the pyGSM Level of Theory object (LOT)")
lot=PyTC.from_options(states=[(1,0),(1,1)],psiw=psiw,do_coupling=True,fnm=filepath)
printcool("Build the pyGSM Potential Energy Surface Object (PES)")
pes1 = PES.from_options(lot=lot,ad_idx=0,multiplicity=1)
pes2 = PES.from_options(lot=lot,ad_idx=1,multiplicity=1)
pes = Avg_PES(pes1,pes2,lot=lot)
printcool("Build the pyGSM Molecule object \n with Translation and Rotation Internal Coordinates (TRIC)")
M = Molecule.from_options(fnm=filepath,PES=pes,coordinate_type="TRIC") | #================================================================#
#| [92m Build the pyGSM Molecule object [0m |#
#| [92m with Translation and Rotation Internal Coordinates (TRIC) [0m |#
#================================================================#
reading cartesian coordinates from file
making primitives from options!
[NodeView((0, 1, 2, 3, 4, 5))]
making primitive Hessian
forming Hessian in basis
| MIT | examples/ipynb_demos/pytc/.ipynb_checkpoints/Basic_API-checkpoint.ipynb | espottesmith/pyGSM |
Section 3: API of Molecule Class | print(M)
print("printing gradient")
pvec1d(M.gradient,5,'f')
M.energy
print("primitive internal coordinates")
print(M.primitive_internal_coordinates)
printcool("primitive number of internal coordinates")
print(M.num_primitives)
printcool("getting the value of a primitive 0")
print(M.primitive_internal_coordinates[0].value(M.xyz))
printcool("printing coordinates in basis")
pmat2d(M.coordinates.T,format='f')
printcool("printing coordinate basis vectors")
pmat2d(M.coord_basis,format='f')
printcool("Wilson B-Matrix (dq_i/dx_j)")
Bmatp = M.coord_obj.Prims.wilsonB(M.xyz)
plt.imshow(Bmatp, cmap=plt.cm.get_cmap('RdBu'))
plt.show()
M.coord_obj.Prims.orderedmakePrimitives(M.xyz,M.coord_obj.options)
printcool("Wilson B-Matrix in coordinate basis")
Bmat = M.coord_obj.wilsonB(M.xyz)
plt.imshow(Bmat, cmap=plt.cm.get_cmap('RdBu'))
plt.show()
printcool("G-Matrix (BB^T in basis of prims)")
G = M.coord_obj.Prims.GMatrix(M.xyz)
plt.imshow(G, cmap=plt.cm.get_cmap('RdBu'))
plt.show()
printcool("G-Matrix in coordinate basis")
G = M.coord_obj.GMatrix(M.xyz)
plt.imshow(G, cmap=plt.cm.get_cmap('RdBu'))
plt.show()
dq = np.zeros((M.num_coordinates,1))
dq[0]=0.1
printcool("taking step and printing new xyz,geom and coordinates")
M.update_xyz(dq)
print
print("new coordinates (in basis)")
pmat2d(M.coordinates.T,format='f')
print
print("new xyz")
pmat2d(M.xyz,4,format='f')
print
print("new geometry")
for atom in M.geometry:
print("%-2s %5.4f %5.4f %5.4f") % (atom[0],atom[1],atom[2],atom[3])
# update coordinate basis
printcool("update coordinate basis")
pmat2d(M.update_coordinate_basis(),format='f') #also used with constraints
print("coords in new basis")
pmat2d(M.coordinates.T,format='f')
print("update Hessian in basis")
pmat2d(M.form_Hessian_in_basis(),format='f')
# copying molecule automatically copies all Data
printcool("copy molecule \n Note that the copy from options is recommended since it properly creates new coord_obj and PES object")
newMolecule = Molecule.copy_from_options(M)
print(newMolecule)
printcool("copy molecule with new geom")
newxyz2 = np.ones(M.xyz.shape)
newxyz2 += M.xyz
newMolecule2 = Molecule.copy_from_options(M,xyz=newxyz2)
print(newMolecule2.xyz)
| #========================================================#
#| [92m copy molecule with new geom [0m |#
#========================================================#
initializing LOT from file
setting primitives from options!
getting cartesian coordinates from geom
getting coord_object from options
[[ 0.9931 1.6993 0.9061]
[ 0.0695 2.2734 0.9061]
[ 1.9167 2.2734 0.9061]
[ 0.9931 0.3683 0.9061]
[ 1.9167 -0.2058 0.9061]
[ 0.0695 -0.2058 0.9061]]
| MIT | examples/ipynb_demos/pytc/.ipynb_checkpoints/Basic_API-checkpoint.ipynb | espottesmith/pyGSM |
Day 2 Assignment - 1 | Q.No.1 # List and its default functions
# (i) Add list element as value of list
# append():
list = ["Jansi","Jessie","Sheela","Thabita"]
list.append("jabamalar")
print(list)
# (ii) Insert():
# Insert at index value 1990
list.insert(1969,1990)
print(list)
# (iii) extend():
List1 = [12, 23, 34, 45]
List2 = [34, 45, 56, 67, 78]
# Add List2 to List1
List1.extend(List2)
print(List1)
# Add List1 to List2 now
List2.extend(List1)
print(List2)
# (iv) count():
List = [12, 23, 45, 34, 45, 34, 45, 56, 67, 78, 45, 89, 90, 45]
print(List.count(45))
# (v) Sum:
List = [12, 23, 34, 45,]
print(sum(List))
Q.No.2 # Dictionary and its default funtions
# (i) Creating a Dictionary
# with Integer Keys
Dict = {1: 'Apple', 2: 'Orange', 3: 'Mangoes'}
print(Dict)
# (ii) Creating a Dictionary
# with Mixed keys
Dict = {'Name': 'Jansi', 1: [100, 101, 102]}
print(Dict)
# (iii) Creating an empty Dictionary
Dict = {}
print(Dict)
# (iv) Creating a Dictionary
# with dict() method
Dict = dict({1: 'Apple', 2: 'Orange', 3:'Mangoes'})
print(Dict)
# (v) Creating a Dictionary
# with each item as a Pair
Dict = dict([(1, 'Apple'), (2, 'Mangoes')])
print(Dict)
Q.No.3 # Sets and its default functions
# (i) Creating a Set
set1 = set()
print(set1)
# (ii) Creating a Set with
# the use of a String
set1 = set("Sneha")
print(set1)
# (iii) Creating a Set with
# the use of Constructor
# (Using object to Store String)
String = 'God is Great'
set1 = set(String)
print(set1)
# (iv) Creating a Set with
# the use of a List
set1 = set(["God", "is", "Great"])
print(set1)
# (v) Creating a Set with
# a List of Numbers
# (Having duplicate values)
set1 = set([1, 2, 4, 4, 3, 3, 3, 6, 5])
print(set1)
Q.No.4 Tuple and its default funtions
# (i) Creating an empty Tuple
Tuple1 = ()
print (Tuple1)
# (ii) Creatting a Tuple
#with the use of string
Tuple1 = ('Wing', 'Star')
print(Tuple1)
# (iii) Creating a Tuple with
# the use of list
list1 = [11, 22, 33, 44, 55]
print(tuple(list1))
# (iv) Creating a Tuple
#with the use of built-in function
Tuple1 = tuple('Jansi')
print(Tuple1)
# (v) Creating a Tuple
#with Mixed Datatype
Tuple1 = (12, 'Maths', 56, 'Science')
print(Tuple1)
Q.No.5 # String and its default functions
# (i) Creating a Tuple
#with Mixed Datatype
Tuple1 = (90, 'Sneha', 79, 'Sharmila')
print(Tuple1)
# (ii) Creating a String
# with double Quotes
String1 = " I love python "
print(String1)
# (iii) Creating a String
# with triple Quotes
String1 = '''I Want to learn python'''
print(String1)
# (iv) Creating String with triple
# Quotes allows multiple lines
String1 = '''Enjoy
the
Life'''
print(String1)
# (v) Creating a string
String1 = "Javascript"
print(String1) | Javascript
| MIT | Assignment - 1 ( Day 2 ).ipynb | jsharmi18421245/LetsUpgrade-pyhton |
Open Street Map Buildings InformationThis notebook demonstrates downloading data from Open Street Map to fill gaps in the [Global Electricity Transmission And Distribution Lines](https://datacatalog.worldbank.org/dataset/derived-map-global-electricity-transmission-and-distribution-lines) (GETD) dataset.To obtain GIS data from Open Street Map of any specified administrative area in the world, the [GeoFabrik](http://download.geofabrik.de/) download server is the easiest solution for querying data at the administrative level.In this notebook, we address obtaining data for areas of AlaskaWe will download the latest version of this data (currently 12/20/21) and extract the commercial, industrial and residential buildings information.Resources* https://wiki.openstreetmap.org/wiki/Map_featuresPower* https://dlr-ve-esy.gitlab.io/esy-osmfilter/main.html* https://stackoverflow.com/questions/66367195/get-all-ways-by-using-esy-osmfilter Mount Drive folderMount Drive folder for saving this data. | from google.colab import drive
drive.mount('/content/drive') | Mounted at /content/drive
| MIT | Packages/Get_Data_From_OSM/Get_open_street_map_lines.ipynb | ryan0124/ACEP_Capstone_Project |
Get packagesInstall packages needed for analysis and import into workspace. | !pip install esy-osmfilter # gives tags and filters to open street map data
!pip install geopandas #to make working with geospatial data in python easier
%load_ext autoreload
%autoreload 2
import configparser, contextlib
import os, sys
import geopandas as gpd
import pandas as pd
from esy.osmfilter import osm_colors as CC
from esy.osmfilter import run_filter
from esy.osmfilter import Node, Way, Relation
from esy.osmfilter import export_geojson
%load_ext autoreload
%autoreload 2 | The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
| MIT | Packages/Get_Data_From_OSM/Get_open_street_map_lines.ipynb | ryan0124/ACEP_Capstone_Project |
To Downlaod the main pbf file (No need to run) | ## NO NEED TO RUN
!wget http://download.geofabrik.de/north-america/us/alaska-latest.osm.pbf -P '/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/' | --2022-03-09 04:44:44-- http://download.geofabrik.de/north-america/us/alaska-latest.osm.pbf
Resolving download.geofabrik.de (download.geofabrik.de)... 95.216.28.113, 116.202.112.212
Connecting to download.geofabrik.de (download.geofabrik.de)|95.216.28.113|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 118348072 (113M) [application/octet-stream]
Saving to: ‘/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/alaska-latest.osm.pbf.4’
alaska-latest.osm.p 100%[===================>] 112.87M 19.8MB/s in 6.9s
2022-03-09 04:44:51 (16.4 MB/s) - ‘/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/alaska-latest.osm.pbf.4’ saved [118348072/118348072]
| MIT | Packages/Get_Data_From_OSM/Get_open_street_map_lines.ipynb | ryan0124/ACEP_Capstone_Project |
Function to download different types of buildings | # Getting residential buildings
def get_res_buildings(area_name):
# Set input/output locations
PBF_inputfile = os.path.join(os.getcwd(), '/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-latest.osm.pbf')
JSON_outputfile = os.path.join(os.getcwd(),'/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-res_buildings.json')
# Pre-filter for all residential buildings
prefilter={Node: {}, Way: {"building":["apartments","bungalow","cabin","detached","dormitory","farm",
"hotel","house","residential","church","garage","garages",],
"tourism":["alpine_hut","apartment","hostel","hotel","motel",
],
"addr":[True,],}, Relation: {}}
whitefilter = []
blackfilter = []
# Create initial data
[Data, _]=run_filter('noname',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=True,
CreateElements=False,
LoadElements=False,
verbose=True)
# # Check that data exists
print(len(Data['Way']))
# # Get residential buildings elements
whitefilter=[(("building","apartments"),), (("building","bungalow"),),(("building","cabin"),),
(("building","detached"),), (("building","dormitory"),),(("building","farm"),),
(("building","hotel"),), (("building","house"),),(("building","residential"),),
(("building","church"),), (("building","garage"),),(("building","garages"),),
(("tourism","alpine_hut"),), (("tourism","apartment"),),(("tourism","hostel"),),
(("tourism","hotel"),), (("tourism","motel"),),(("addr",True),),
]
blackfilter=[((),),]
# Apply filter
[Data, Elements]=run_filter('powerlines',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=False,
CreateElements=True,
LoadElements=False,
verbose=True)
print(len(Data['Way']))
# Export data to geojson
export_geojson(Elements['powerlines']['Way'],Data, filename='/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-res_buildings.geojson',jsontype='Line')
# Read data into geopandas
gdf = gpd.read_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-res_buildings.geojson')
# Write as shapefile
gdf.to_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-res_buildings.shp')
#Plot output
gdf.plot()
# Getting commercial buildings
def get_com_buildings(area_name):
# Set input/output locations
PBF_inputfile = os.path.join(os.getcwd(), '/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-latest.osm.pbf')
JSON_outputfile = os.path.join(os.getcwd(),'/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-com_buildings.json')
# Pre-filter for all commercial buildings
prefilter={Node: {"amenity":["charging_staion","atm","strip_club",],}, Way: {"aeroway":["terminal",],"amenity":["bar","cafe","fast_food","food_court","pub","restaurant","college","kindergarten",
"library","school","university","bus_station","car_rental","car_wash",
"fuel","bank","clinic","dentist",
"doctor","hospital","nursing_home","pharmacy","socail_facility",
"veterinary","brothel","casino","cinema","community_centre",
"conference_centre","gambling","love_hotel","night_club",
"planetarium","studio","swingerclub",
"theatre","courthouse","embassy","fire_station",
"police","post_office","prison","townhall",
"creamatorium","internet_cafe","place_of_worship",],
"building":["commerical","office","retail","supermarket","warehouse","bakehouse","civic","college","fire_station",
"government","hospital","kindergarten","public","school","train_staion","transportaion",
"university","stadium","parking",],
"leisure":["adult_gaming_centre","amusement_arcade","beach_resort","fitness_centre",
"sports_centre","stadium","summer_camp",],
"office":["accountant","association","charity","company","comsulting","courier",
"dipomatic","educational_institution","employment_agency","engineer",
"estate_agent","financial","forestry","foundation","government","insurance",
"lawyer","logistics","moving_company","ngo","political_party",
"property_management","religion","research","security","tax_advisor","telecommunication",
"union","water_utility","yes"],
"shop":[True],
"tourism":["aquarium","gallery","museum","zoo","yes"],
"addr":[True,],}, Relation: {}}
whitefilter = []
blackfilter = []
# Create initial data
[Data, _]=run_filter('noname',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=True,
CreateElements=False,
LoadElements=False,
verbose=True)
# # Check that data exists
print(len(Data['Node']))
# # Get commercial buildings elements
whitefilter=[(("aeroway","terminal"),), (("amenity","bar"),), (("amenity","cafe"),), (("amenity","fast_food"),), (("amenity","food_court"),), (("amenity","pub"),), (("amenity","restaurant"),),
(("amenity","college"),), (("amenity","kindergarten"),), (("amenity","library"),), (("amenity","school"),), (("amenity","university"),), (("amenity","bus_station"),),
(("amenity","car_rental"),), (("amenity","car_wash"),), (("amenity","fuel"),), (("amenity","bank"),), (("amenity","clinic"),), (("amenity","dentist"),), (("amenity","doctor"),),
(("amenity","hospital"),), (("amenity","nursing_home"),), (("amenity","pharmacy"),), (("amenity","socail_facility"),), (("amenity","veterinary"),), (("amenity","brothel"),),
(("amenity","casino"),), (("amenity","cinema"),), (("amenity","community_centre"),), (("amenity","conference_centre"),), (("amenity","gambling"),), (("amenity","love_hotel"),),
(("amenity","night_club"),), (("amenity","planetarium"),), (("amenity","studio"),), (("amenity","swingerclub"),), (("amenity","theatre"),), (("amenity","courthouse"),),
(("amenity","embassy"),), (("amenity","fire_station"),), (("amenity","police"),), (("amenity","post_office"),), (("amenity","prison"),), (("amenity","townhall"),), (("amenity","creamatorium"),),
(("amenity","internet_cafe"),), (("amenity","place_of_worship"),), (("building","commerical"),), (("building","office"),), (("building","retail"),), (("building","supermarket"),),
(("building","warehouse"),), (("building","bakehouse"),), (("building","civic"),), (("building","college"),), (("building","fire_station"),), (("building","government"),),
(("building","hospital"),), (("building","kindergarten"),), (("building","public"),), (("building","school"),), (("building","train_staion"),), (("building","transportaion"),),
(("building","university"),), (("building","stadium"),), (("building","parking"),), (("leisure","adult_gaming_centre"),), (("leisure","amusement_arcade"),), (("leisure","beach_resort"),),
(("leisure","amusement_arcade"),), (("leisure","amusement_arcade"),), (("leisure","amusement_arcade"),), (("leisure","amusement_arcade"),), (("leisure","amusement_arcade"),),
(("leisure","fitness_centre"),), (("leisure","sports_centre"),), (("leisure","stadium"),), (("leisure","summer_camp"),), (("office","accountant"),), (("office","association"),),
(("office","charity"),), (("office","company"),), (("office","comsulting"),), (("office","courier"),), (("office","dipomatic"),), (("office","educational_institution"),),
(("office","employment_agency"),), (("office","engineer"),), (("office","estate_agent"),), (("office","financial"),), (("office","forestry"),), (("office","foundation"),),
(("office","government"),), (("office","insurance"),), (("office","lawyer"),), (("office","logistics"),), (("office","moving_company"),), (("office","ngo"),),
(("office","political_party"),), (("office","property_management"),), (("office","religion"),), (("office","research"),), (("office","security"),), (("office","tax_advisor"),),
(("office","telecommunication"),), (("office","union"),), (("office","water_utility"),), (("office","yes"),), (("shop",True),), (("tourism","aquarium"),), (("tourism","gallery"),),
(("tourism","museum"),), (("tourism","yes"),), (("addr",True),),
]
blackfilter=[((),),]
# Apply filter
[Data, Elements]=run_filter('powerlines',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=False,
CreateElements=True,
LoadElements=False,
verbose=True)
print(len(Data['Node']))
# Export data to geojson
export_geojson(Elements['powerlines']['Way'],Data, filename='/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-com_buildings.geojson',jsontype='Line')
# Read data into geopandas
gdf = gpd.read_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-com_buildings.geojson')
# Write as shapefile
gdf.to_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-com_buildings.shp')
#Plot output
gdf.plot()
# Getting industrial buildings
def get_ind_buildings(area_name):
# Set input/output locations
PBF_inputfile = os.path.join(os.getcwd(), '/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-latest.osm.pbf')
JSON_outputfile = os.path.join(os.getcwd(),'/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-ind_buildings.json')
# Pre-filter for all industrial buildings
prefilter={Node: {}, Way: {"building":["industrial","digestor","service","transformer_tower","water_tower",
"military",],
"historic":["creamery",],
"man_made":["lighthouse","monitoring_station","observatory",
"pumping_staition","wastewater_plant","water_works",
"works",],
"military":["base","barracks",],
"public_transport":["station",],
"railway":["station",],
"telecom":["data_center",],
"water":["wastewater",],
"waterway":["dock","boatyard",],
"addr":[True,],
}, Relation: {}}
whitefilter = []
blackfilter = []
# Create initial data
[Data, _]=run_filter('noname',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=True,
CreateElements=False,
LoadElements=False,
verbose=True)
# # Check that data exists
print(len(Data['Node']))
# Get industrial buildings elements
whitefilter=[(("building","industrial"),), (("building","digestor"),), (("building","service"),), (("building","transformer_tower"),), (("building","water_tower"),), (("building","military"),),
(("historic","creamery"),), (("military","barracks"),), (("military","base"),), (("public_transport","station"),), (("railway","station"),), (("telecom","data_center"),),
(("water","wastewater"),), (("waterway","dock"),), (("waterway","boatyard"),), (("addr",True),), (("man_made","lighthouse"),), (("man_made","monitoring_station"),),
(("man_made","observatory"),), (("man_made","wastewater_plant"),), (("man_made","pumping_staition"),), (("man_made","water_works"),), (("man_made","works"),),
]
blackfilter=[((),),]
# Apply filter
[Data, Elements]=run_filter('powerlines',
PBF_inputfile,
JSON_outputfile,
prefilter,
whitefilter,
blackfilter,
NewPreFilterData=False,
CreateElements=True,
LoadElements=False,
verbose=True)
print(len(Data['Node']))
#data = Data['Way']
#df = pd.DataFrame(data)
#return (df)
#return (Data['Way'])
# Export data to geojson
export_geojson(Elements['powerlines']['Way'],Data, filename='/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-ind_buildings.geojson',jsontype='Line')
# Read data into geopandas
gdf = gpd.read_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-ind_buildings.geojson')
# Write as shapefile
gdf.to_file('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/'+area_name+'-ind_buildings.shp')
#Plot output
gdf.plot()
# commercial buildings data
get_com_buildings('alaska')
# industrial buildings data
get_ind_buildings('alaska')
# residential buildings data
get_res_buildings('alaska')
import pandas as pd
#Check the size of the data
res_data=pd.read_json('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/alaska-res_buildings.json')
len(res_data)
#Check the size of the data
com_data=pd.read_json('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/alaska-com_buildings.json')
len(com_data)
#Check the size of the data
ind_data=pd.read_json('/content/drive/MyDrive/ACEP_Data_Team/Railbelt_line/Script_data/alaska-ind_buildings.json')
len(ind_data) | _____no_output_____ | MIT | Packages/Get_Data_From_OSM/Get_open_street_map_lines.ipynb | ryan0124/ACEP_Capstone_Project |
TensorFlow Neural Network Lab In this lab, you'll use all the tools you learned from *Introduction to TensorFlow* to label images of English letters! The data you are using, notMNIST, consists of images of a letter from A to J in different fonts.The above images are a few examples of the data you'll be training on. After training the network, you will compare your prediction model against test data. Your goal, by the end of this lab, is to make predictions against that test set with at least an 80% accuracy. Let's jump in! To start this lab, you first need to import all the necessary modules. Run the code below. If it runs successfully, it will print "`All modules imported`". | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All modules imported.') | All modules imported.
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this data, 15,000 images for each label (A-J). | ## 이미 로컬로 파일을 다운로드 받았으므로 이제 이것은 돌리지 않아도 됨.
def download(url, file):
"""
Download file from <url>
:param url: URL to file
:param file: Local file path
"""
if not os.path.isfile(file):
print('Downloading ' + file + '...')
urlretrieve(url, file)
print('Download Finished')
# Download the training and test dataset.
download('https://s3.amazonaws.com/udacity-sdc/notMNIST_train.zip', 'notMNIST_train.zip')
download('https://s3.amazonaws.com/udacity-sdc/notMNIST_test.zip', 'notMNIST_test.zip')
# Make sure the files aren't corrupted
assert hashlib.md5(open('notMNIST_train.zip', 'rb').read()).hexdigest() == 'c8673b3f28f489e9cdf3a3d74e2ac8fa',\
'notMNIST_train.zip file is corrupted. Remove the file and try again.'
assert hashlib.md5(open('notMNIST_test.zip', 'rb').read()).hexdigest() == '5d3c7e653e63471c88df796156a9dfa9',\
'notMNIST_test.zip file is corrupted. Remove the file and try again.'
# Wait until you see that all files have been downloaded.
print('All files downloaded.')
def uncompress_features_labels(file):
"""
Uncompress features and labels from a zip file
:param file: The zip file to extract the data from
"""
features = []
labels = []
with ZipFile(file) as zipf:
# Progress Bar
filenames_pbar = tqdm(zipf.namelist(), unit='files')
# Get features and labels from all files
for filename in filenames_pbar:
# Check if the file is a directory
if not filename.endswith('/'):
with zipf.open(filename) as image_file:
image = Image.open(image_file)
image.load()
# Load image data as 1 dimensional array
# We're using float32 to save on memory space
feature = np.array(image, dtype=np.float32).flatten()
# Get the the letter from the filename. This is the letter of the image.
label = os.path.split(filename)[1][0]
features.append(feature)
labels.append(label)
return np.array(features), np.array(labels)
# Get the features and labels from the zip files
train_features, train_labels = uncompress_features_labels('notMNIST_train.zip')
test_features, test_labels = uncompress_features_labels('notMNIST_test.zip')
# Limit the amount of data to work with a docker container
docker_size_limit = 150000
train_features, train_labels = resample(train_features, train_labels, n_samples=docker_size_limit)
# Set flags for feature engineering. This will prevent you from skipping an important step.
is_features_normal = False
is_labels_encod = False
# Wait until you see that all features and labels have been uncompressed.
print('All features and labels uncompressed.') | 100%|██████████████████████████████████████████████████████████| 210001/210001 [04:33<00:00, 767.96files/s]
100%|████████████████████████████████████████████████████████████| 10001/10001 [00:10<00:00, 937.99files/s]
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
Problem 1The first problem involves normalizing the features for your training and test data.Implement Min-Max scaling in the `normalize_grayscale()` function to a range of `a=0.1` and `b=0.9`. After scaling, the values of the pixels in the input data should range from 0.1 to 0.9.Since the raw notMNIST image data is in [grayscale](https://en.wikipedia.org/wiki/Grayscale), the current values range from a min of 0 to a max of 255.Min-Max Scaling:$X'=a+{\frac {\left(X-X_{\min }\right)\left(b-a\right)}{X_{\max }-X_{\min }}}$*If you're having trouble solving problem 1, you can view the solution [here](https://github.com/udacity/deep-learning/blob/master/intro-to-tensorflow/intro_to_tensorflow_solution.ipynb).* | # Problem 1 - Implement Min-Max scaling for grayscale image data
def normalize_grayscale(image_data):
"""
Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]
:param image_data: The image data to be normalized
:return: Normalized image data
"""
# TODO: Implement Min-Max scaling for grayscale image data
a, b = 0.1, 0.9
min_value, max_value = 0, 255
return a + ((image_data - min_value) * (b - a)) / (max_value - min_value)
### DON'T MODIFY ANYTHING BELOW ###
# Test Cases
np.testing.assert_array_almost_equal(
normalize_grayscale(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 255])),
[0.1, 0.103137254902, 0.106274509804, 0.109411764706, 0.112549019608, 0.11568627451, 0.118823529412, 0.121960784314,
0.125098039216, 0.128235294118, 0.13137254902, 0.9],
decimal=3)
np.testing.assert_array_almost_equal(
normalize_grayscale(np.array([0, 1, 10, 20, 30, 40, 233, 244, 254,255])),
[0.1, 0.103137254902, 0.13137254902, 0.162745098039, 0.194117647059, 0.225490196078, 0.830980392157, 0.865490196078,
0.896862745098, 0.9])
if not is_features_normal:
train_features = normalize_grayscale(train_features)
test_features = normalize_grayscale(test_features)
is_features_normal = True
print('Tests Passed!')
if not is_labels_encod:
# Turn labels into numbers and apply One-Hot Encoding
encoder = LabelBinarizer()
encoder.fit(train_labels)
train_labels = encoder.transform(train_labels)
test_labels = encoder.transform(test_labels)
# Change to float32, so it can be multiplied against the features in TensorFlow, which are float32
train_labels = train_labels.astype(np.float32)
test_labels = test_labels.astype(np.float32)
is_labels_encod = True
print('Labels One-Hot Encoded')
assert is_features_normal, 'You skipped the step to normalize the features'
assert is_labels_encod, 'You skipped the step to One-Hot Encode the labels'
# Get randomized datasets for training and validation
train_features, valid_features, train_labels, valid_labels = train_test_split(
train_features,
train_labels,
test_size=0.05,
random_state=832289)
print('Training features and labels randomized and split.')
# Save the data for easy access
pickle_file = 'notMNIST.pickle'
if not os.path.isfile(pickle_file):
print('Saving data to pickle file...')
try:
with open('notMNIST.pickle', 'wb') as pfile:
pickle.dump(
{
'train_dataset': train_features,
'train_labels': train_labels,
'valid_dataset': valid_features,
'valid_labels': valid_labels,
'test_dataset': test_features,
'test_labels': test_labels,
},
pfile, pickle.HIGHEST_PROTOCOL)
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
print('Data cached in pickle file.') | Saving data to pickle file...
Data cached in pickle file.
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
CheckpointAll your progress is now saved to the pickle file. If you need to leave and comeback to this lab, you no longer have to start from the beginning. Just run the code block below and it will load all the data and modules required to proceed. | %matplotlib inline
# Load the modules
import pickle
import math
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import matplotlib.pyplot as plt
# Reload the data
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f)
train_features = pickle_data['train_dataset']
train_labels = pickle_data['train_labels']
valid_features = pickle_data['valid_dataset']
valid_labels = pickle_data['valid_labels']
test_features = pickle_data['test_dataset']
test_labels = pickle_data['test_labels']
del pickle_data # Free up memory
print('Data and modules loaded.') | Data and modules loaded.
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
Problem 2Now it's time to build a simple neural network using TensorFlow. Here, your network will be just an input layer and an output layer.For the input here the images have been flattened into a vector of $28 \times 28 = 784$ features. Then, we're trying to predict the image digit so there are 10 output units, one for each label. Of course, feel free to add hidden layers if you want, but this notebook is built to guide you through a single layer network. For the neural network to train on your data, you need the following float32 tensors: - `features` - Placeholder tensor for feature data (`train_features`/`valid_features`/`test_features`) - `labels` - Placeholder tensor for label data (`train_labels`/`valid_labels`/`test_labels`) - `weights` - Variable Tensor with random numbers from a truncated normal distribution. - See `tf.truncated_normal()` documentation for help. - `biases` - Variable Tensor with all zeros. - See `tf.zeros()` documentation for help.*If you're having trouble solving problem 2, review "TensorFlow Linear Function" section of the class. If that doesn't help, the solution for this problem is available [here](intro_to_tensorflow_solution.ipynb).* | # All the pixels in the image (28 * 28 = 784)
features_count = 784
# All the labels
labels_count = 10
# TODO: Set the features and labels tensors
features = tf.placeholder(tf.float32)
labels = tf.placeholder(tf.float32)
# TODO: Set the weights and biases tensors
weights = tf.Variable(tf.truncated_normal((features_count, labels_count)))
biases = tf.Variable(tf.zeros(labels_count))
### DON'T MODIFY ANYTHING BELOW ###
#Test Cases
from tensorflow.python.ops.variables import Variable
assert features._op.name.startswith('Placeholder'), 'features must be a placeholder'
assert labels._op.name.startswith('Placeholder'), 'labels must be a placeholder'
assert isinstance(weights, Variable), 'weights must be a TensorFlow variable'
assert isinstance(biases, Variable), 'biases must be a TensorFlow variable'
assert features._shape == None or (\
features._shape.dims[0].value is None and\
features._shape.dims[1].value in [None, 784]), 'The shape of features is incorrect'
assert labels._shape == None or (\
labels._shape.dims[0].value is None and\
labels._shape.dims[1].value in [None, 10]), 'The shape of labels is incorrect'
assert weights._variable._shape == (784, 10), 'The shape of weights is incorrect'
assert biases._variable._shape == (10), 'The shape of biases is incorrect'
assert features._dtype == tf.float32, 'features must be type float32'
assert labels._dtype == tf.float32, 'labels must be type float32'
# Feed dicts for training, validation, and test session
train_feed_dict = {features: train_features, labels: train_labels}
valid_feed_dict = {features: valid_features, labels: valid_labels}
test_feed_dict = {features: test_features, labels: test_labels}
# Linear Function WX + b
logits = tf.matmul(features, weights) + biases
prediction = tf.nn.softmax(logits)
# Cross entropy
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)
# Training loss
loss = tf.reduce_mean(cross_entropy)
# Create an operation that initializes all variables
init = tf.global_variables_initializer()
# Test Cases
with tf.Session() as session:
session.run(init)
session.run(loss, feed_dict=train_feed_dict)
session.run(loss, feed_dict=valid_feed_dict)
session.run(loss, feed_dict=test_feed_dict)
biases_data = session.run(biases)
assert not np.count_nonzero(biases_data), 'biases must be zeros'
print('Tests Passed!')
# Determine if the predictions are correct
is_correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))
# Calculate the accuracy of the predictions
accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))
print('Accuracy function created.') | Accuracy function created.
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
Problem 3Below are 2 parameter configurations for training the neural network. In each configuration, one of the parameters has multiple options. For each configuration, choose the option that gives the best acccuracy.Parameter configurations:Configuration 1* **Epochs:** 1* **Learning Rate:** * 0.8 * 0.5 * 0.1 * 0.05 * 0.01Configuration 2* **Epochs:** * 1 * 2 * 3 * 4 * 5* **Learning Rate:** 0.2The code will print out a Loss and Accuracy graph, so you can see how well the neural network performed.*If you're having trouble solving problem 3, you can view the solution [here](intro_to_tensorflow_solution.ipynb).* | # Change if you have memory restrictions
batch_size = 128
# TODO: Find the best parameters for each configuration
epochs = 10
learning_rate = 0.08
### DON'T MODIFY ANYTHING BELOW ###
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
# The accuracy measured against the validation set
validation_accuracy = 0.0
# Measurements use for graphing loss and accuracy
log_batch_step = 50
batches = []
loss_batch = []
train_acc_batch = []
valid_acc_batch = []
with tf.Session() as session:
session.run(init)
batch_count = int(math.ceil(len(train_features)/batch_size))
for epoch_i in range(epochs):
# Progress bar
batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')
# The training cycle
for batch_i in batches_pbar:
# Get a batch of training features and labels
batch_start = batch_i*batch_size
batch_features = train_features[batch_start:batch_start + batch_size]
batch_labels = train_labels[batch_start:batch_start + batch_size]
# Run optimizer and get loss
_, l = session.run(
[optimizer, loss],
feed_dict={features: batch_features, labels: batch_labels})
# Log every 50 batches
if not batch_i % log_batch_step:
# Calculate Training and Validation accuracy
training_accuracy = session.run(accuracy, feed_dict=train_feed_dict)
validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)
# Log batches
previous_batch = batches[-1] if batches else 0
batches.append(log_batch_step + previous_batch)
loss_batch.append(l)
train_acc_batch.append(training_accuracy)
valid_acc_batch.append(validation_accuracy)
# Check accuracy against Validation data
validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)
loss_plot = plt.subplot(211)
loss_plot.set_title('Loss')
loss_plot.plot(batches, loss_batch, 'g')
loss_plot.set_xlim([batches[0], batches[-1]])
acc_plot = plt.subplot(212)
acc_plot.set_title('Accuracy')
acc_plot.plot(batches, train_acc_batch, 'r', label='Training Accuracy')
acc_plot.plot(batches, valid_acc_batch, 'x', label='Validation Accuracy')
acc_plot.set_ylim([0, 1.0])
acc_plot.set_xlim([batches[0], batches[-1]])
acc_plot.legend(loc=4)
plt.tight_layout()
plt.show()
print('Validation accuracy at {}'.format(validation_accuracy)) | Epoch 1/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:15<00:00, 14.83batches/s]
Epoch 2/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:00<00:00, 18.32batches/s]
Epoch 3/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:04<00:00, 17.37batches/s]
Epoch 4/10: 100%|████████████████████████████████████████████████| 1114/1114 [00:58<00:00, 19.09batches/s]
Epoch 5/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:02<00:00, 17.69batches/s]
Epoch 6/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:10<00:00, 15.76batches/s]
Epoch 7/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:20<00:00, 13.76batches/s]
Epoch 8/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:00<00:00, 18.38batches/s]
Epoch 9/10: 100%|████████████████████████████████████████████████| 1114/1114 [00:58<00:00, 18.90batches/s]
Epoch 10/10: 100%|████████████████████████████████████████████████| 1114/1114 [01:06<00:00, 16.67batches/s]
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
Best Hyper-parameters1. **epochs**: 1, **Learning Rate**: 0.1 -> **Validation accuracy**: 0.7342. **epochs**: 5, **Learning Rate**: 0.2 -> **Validation accuracy**: 0.7603. **epochs**: 5, **Learning Rate**: 0.1 -> **Validation accuracy**: 0.766 TestYou're going to test your model against your hold out dataset/testing data. This will give you a good indicator of how well the model will do in the real world. You should have a test accuracy of at least 80%. | ### DON'T MODIFY ANYTHING BELOW ###
# The accuracy measured against the test set
test_accuracy = 0.0
with tf.Session() as session:
session.run(init)
batch_count = int(math.ceil(len(train_features)/batch_size))
for epoch_i in range(epochs):
# Progress bar
batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')
# The training cycle
for batch_i in batches_pbar:
# Get a batch of training features and labels
batch_start = batch_i*batch_size
batch_features = train_features[batch_start:batch_start + batch_size]
batch_labels = train_labels[batch_start:batch_start + batch_size]
# Run optimizer
_ = session.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})
# Check accuracy against Test data
test_accuracy = session.run(accuracy, feed_dict=test_feed_dict)
assert test_accuracy >= 0.80, 'Test accuracy at {}, should be equal to or greater than 0.80'.format(test_accuracy)
print('Nice Job! Test Accuracy is {}'.format(test_accuracy)) | Epoch 1/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 149.98batches/s]
Epoch 2/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 158.93batches/s]
Epoch 3/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 157.26batches/s]
Epoch 4/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:08<00:00, 137.08batches/s]
Epoch 5/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:10<00:00, 110.91batches/s]
Epoch 6/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:08<00:00, 126.60batches/s]
Epoch 7/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 143.13batches/s]
Epoch 8/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 151.78batches/s]
Epoch 9/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 141.90batches/s]
Epoch 10/10: 100%|███████████████████████████████████████████████| 1114/1114 [00:07<00:00, 158.18batches/s]
| MIT | intro-to-tensorflow/intro_to_tensorflow.ipynb | postBG/deep-learning |
**Question 1:** Write a function `square` that squares its argument. | def square(x):
return x**2
grader.check("q1") | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**Question 2:** Write a function `negate` that negates its argument. | def negate(x):
return not x
grader.check("q2") | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**Question 3:** Assign `x` to the negation of `[]`. Use `negate`. | x = negate([])
x
grader.check("q3") | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**Question 4:** Assign `x` to the square of 6.25. Use `square`. | x = square(6.25)
x
grader.check("q4") | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**Question 5:** Plot $f(x) = \cos (x e^x)$ on $(0,10)$. | x = np.linspace(0, 10, 100)
y = np.cos(x * np.exp(x))
plt.plot(x, y) | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**Question 6:** Write a non-recursive infinite generator for the Fibonacci sequence `fiberator`. | def fiberator():
yield 0
yield 1
a, b = 0, 1
while True:
a, b = b, a + b
yield a
grader.check("q6") | _____no_output_____ | BSD-3-Clause | test/test-grade/notebooks/fails6H.ipynb | chrispyles/otter-grader |
**FAQ:**- weighting do sampler `dowhy.do_samplers.weighting_sampler.WeightingSampler` 是什么？应该是一个使用倾向得分估计(Logistic Regression) 的判别模型。 Do-sampler 简介--- by Adam Kelleher, Heyang Gong 编译The "do-sampler" is a new feature in DoWhy. 尽管大多数以潜在结果为导向的估算器都专注于估计 the specific contrast $E[Y_0 - Y_1]$, Pearlian inference 专注于更基本的因果量，如反事实结果的分布$P(Y^x = y)$, 它可以用来得出其他感兴趣的统计信息。 通常，很难非参数地表示概率分布。即使可以，您也不想 gloss over finite-sample problems with you data you used to generate it. 考虑到这些问题，我们决定通过使用称为“ do-sampler”的对象从它们中进行采样来表示干预性分布。利用这些样本，我们可以希望 compute finite-sample statistics of our interventional data. 如果我们 bootstrap 许多这样的样本，我们甚至可以期待得到这些统计量的 good sampling distributions. The user should not 这仍然是一个活跃的研究领域，so you should be careful about being too confident in bootstrapped error bars from do-samplers.Note that do samplers sample from the outcome distribution, and so will vary significantly from sample to sample. To use them to compute outcomes, 我们推荐 generate several such samples to get an idea of the posterior variance of your statistic of interest. Pearlian 干预Following the notion of an intervention in a Pearlian causal model, 我们的 do-samplers 顺序执行如下步骤:1. Disrupt causes2. Make Effective3. Propagate and sample 在第一阶段，我们设想 cutting the in-edges to all of the variables we're intervening on. 在第二阶段，我们将这些变量的值设置为 their interventional quantities。在第三阶段，我们通过模型向前传播该值 to compute interventional outcomes with a sampling procedure.在实践中，我们可以通过多种方式来实现这些步骤。 They're most explicit when we build the model as a linear bayesian network in PyMC3, which is what underlies the MCMC do sampler. In that case, we fit one bayesian network to the data, then construct a new network representing the interventional network. The structural equations are set with the parameters fit in the initial network, and we sample from that new network to get our do sample.In the **weighting do sampler**, we abstractly think of "disrupting the causes" by accounting for selection into the causal state through propensity score estimation. These scores contain the information used to block back-door paths, and so have the same statistics effect as cutting edges into the causal state. We make the treatment effective by selecting the subset of our data set with the correct value of the causal state. Finally, we generated a weighted random sample using inverse propensity weighting to get our do sample.您可以通过其他方法来实现这三个步骤, but the formula is the same. We've abstracted them out as abstract class methods which you should override if you'd like to create your own do sampler!我们实现的 do sampler 有三个特点： Statefulness, Integration 和 Specifying interventions. StatefulnessThe do sampler when accessed through the high-level pandas API is stateless by default. This makes it intuitive to work with, and you can generate different samples with repeated calls to the `pandas.DataFrame.causal.do`. It can be made stateful, which is sometimes useful. 我们之前提到的三阶段流程已 is implemented by passing an internal `pandas.DataFrame` through each of the three stages, but regarding it as temporary. The internal dataframe is reset by default before returning the result.It can be much more efficient to maintain state in the do sampler between generating samples. This is especially true when step 1 requires fitting an expensive model, as is the case with the MCMC do sampler, the kernel density sampler, and the weighting sampler. (只拟合一次模型) Instead of re-fitting the model for each sample, you'd like to fit it once, and then generate many samples from the do sampler. You can do this by setting the kwarg `stateful=True` when you call the `pandas.DataFrame.causal.do` method. To reset the state of the dataframe (deleting the model as well as the internal dataframe), you can call the `pandas.DataFrame.causal.reset` method.Through the lower-level API, the sampler 默认是无需申明的。 The assumption is that a "power user" who is using the low-level API will want more control over the sampling process. In this case, state is carried by internal dataframe `self._df`, which is a copy of the dataframe passed on instantiation. The original dataframe is kept in `self._data`, and is used when the user resets state. IntegrationThe do-sampler is built on top of the identification abstraction used throughout DoWhy. It uses a `dowhy.CausalModel` to perform identification, and builds any models it needs automatically using this identification. Specifying InterventionsThere is a kwarg on the `dowhy.do_sampler.DoSampler` object called `keep_original_treatment`. While an intervention might be to set all units treatment values to some specific value, it's often natural to keep them set as they were, and instead remove confounding bias during effect estimation. If you'd prefer not to specify an intervention, you can set the kwarg like `keep_original_treatment=True`, and the second stage of the 3-stage process will be skipped. In that case, any intervention specified on sampling will be ignored.If the `keep_original_treatment` flag is set to false (it is by default), then you must specify an intervention when you sample from the do sampler. For details, see the demo below! Demo首先，让我们生成一些数据和一个因果模型。Here, Z confounds our causal state, D, with the outcome, Y. | import os, sys
sys.path.append(os.path.abspath("../../../"))
import numpy as np
import pandas as pd
import dowhy.api
N = 5000
z = np.random.uniform(size=N)
d = np.random.binomial(1., p=1./(1. + np.exp(-5. * z)))
y = 2. * z + d + 0.1 * np.random.normal(size=N)
df = pd.DataFrame({'Z': z, 'D': d, 'Y': y})
(df[df.D == 1].mean() - df[df.D == 0].mean())['Y'] | _____no_output_____ | MIT | docs/source/example_notebooks/do_sampler_demo.ipynb | Causal-Inference-ZeroToAll/dowhy |
结果比真实的因果效应高 60%. 那么，让我们为这些数据建立因果模型。 | from dowhy import CausalModel
causes = ['D']
outcomes = ['Y']
common_causes = ['Z']
model = CausalModel(df,
causes,
outcomes,
common_causes=common_causes,
proceed_when_unidentifiable=True) | WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.
INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['D'] on outcome ['Y']
| MIT | docs/source/example_notebooks/do_sampler_demo.ipynb | Causal-Inference-ZeroToAll/dowhy |
Now that we have a model, we can try to identify the causal effect. | identification = model.identify_effect() | INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['U', 'Z']
WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.
INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.
INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]
| MIT | docs/source/example_notebooks/do_sampler_demo.ipynb | Causal-Inference-ZeroToAll/dowhy |
Identification works! We didn't actually need to do this yet, since it will happen internally with the do sampler, but it can't hurt to check that identification works before proceeding. Now, let's build the sampler. | from dowhy.do_samplers.weighting_sampler import WeightingSampler
sampler = WeightingSampler(df,
causal_model=model,
keep_original_treatment=True,
variable_types={'D': 'b', 'Z': 'c', 'Y': 'c'}) | INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['U', 'Z']
WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.
INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.
INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]
INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.
INFO:dowhy.do_sampler:Caution: do samplers assume iid data.
| MIT | docs/source/example_notebooks/do_sampler_demo.ipynb | Causal-Inference-ZeroToAll/dowhy |
Now, we can just sample from the interventional distribution! Since we set the `keep_original_treatment` flag to `False`, any treatment we pass here will be ignored. Here, we'll just pass `None` to acknowledge that we know we don't want to pass anything.If you'd prefer to specify an intervention, you can just put the interventional value here instead as a list or numpy array. | interventional_df = sampler.do_sample(None)
(interventional_df[interventional_df.D == 1].mean() - interventional_df[interventional_df.D == 0].mean())['Y'] | _____no_output_____ | MIT | docs/source/example_notebooks/do_sampler_demo.ipynb | Causal-Inference-ZeroToAll/dowhy |
Introduction to PyTorch***********************Introduction to Torch's tensor library======================================All of deep learning is computations on tensors, which aregeneralizations of a matrix that can be indexed in more than 2dimensions. We will see exactly what this means in-depth later. First,lets look what we can do with tensors. | # Author: Robert Guthrie
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
torch.manual_seed(1) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Creating Tensors~~~~~~~~~~~~~~~~Tensors can be created from Python lists with the torch.Tensor()function. | # torch.tensor(data) creates a torch.Tensor object with the given data.
V_data = [1., 2., 3.]
V = torch.tensor(V_data)
print(V)
# Creates a matrix
M_data = [[1., 2., 3.], [4., 5., 6]]
M = torch.tensor(M_data)
print(M)
# Create a 3D tensor of size 2x2x2.
T_data = [[[1., 2.], [3., 4.]],
[[5., 6.], [7., 8.]]]
T = torch.tensor(T_data)
print(T) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
What is a 3D tensor anyway? Think about it like this. If you have avector, indexing into the vector gives you a scalar. If you have amatrix, indexing into the matrix gives you a vector. If you have a 3Dtensor, then indexing into the tensor gives you a matrix!A note on terminology:when I say "tensor" in this tutorial, it refersto any torch.Tensor object. Matrices and vectors are special cases oftorch.Tensors, where their dimension is 1 and 2 respectively. When I amtalking about 3D tensors, I will explicitly use the term "3D tensor". | # Index into V and get a scalar (0 dimensional tensor)
print(V[0])
# Get a Python number from it
print(V[0].item())
# Index into M and get a vector
print(M[0])
# Index into T and get a matrix
print(T[0]) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
You can also create tensors of other datatypes. The default, as you cansee, is Float. To create a tensor of integer types, trytorch.LongTensor(). Check the documentation for more data types, butFloat and Long will be the most common. You can create a tensor with random data and the supplied dimensionalitywith torch.randn() | x = torch.randn((3, 4, 5))
print(x) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Operations with Tensors~~~~~~~~~~~~~~~~~~~~~~~You can operate on tensors in the ways you would expect. | x = torch.tensor([1., 2., 3.])
y = torch.tensor([4., 5., 6.])
z = x + y
print(z) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
See `the documentation `__ for acomplete list of the massive number of operations available to you. Theyexpand beyond just mathematical operations.One helpful operation that we will make use of later is concatenation. | # By default, it concatenates along the first axis (concatenates rows)
x_1 = torch.randn(2, 5)
y_1 = torch.randn(3, 5)
z_1 = torch.cat([x_1, y_1])
print(z_1)
# Concatenate columns:
x_2 = torch.randn(2, 3)
y_2 = torch.randn(2, 5)
# second arg specifies which axis to concat along
z_2 = torch.cat([x_2, y_2], 1)
print(z_2)
# If your tensors are not compatible, torch will complain. Uncomment to see the error
# torch.cat([x_1, x_2]) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Reshaping Tensors~~~~~~~~~~~~~~~~~Use the .view() method to reshape a tensor. This method receives heavyuse, because many neural network components expect their inputs to havea certain shape. Often you will need to reshape before passing your datato the component. | x = torch.randn(2, 3, 4)
print(x)
print(x.view(2, 12)) # Reshape to 2 rows, 12 columns
# Same as above. If one of the dimensions is -1, its size can be inferred
print(x.view(2, -1)) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Computation Graphs and Automatic Differentiation================================================The concept of a computation graph is essential to efficient deeplearning programming, because it allows you to not have to write theback propagation gradients yourself. A computation graph is simply aspecification of how your data is combined to give you the output. Sincethe graph totally specifies what parameters were involved with whichoperations, it contains enough information to compute derivatives. Thisprobably sounds vague, so let's see what is going on using thefundamental flag ``requires_grad``.First, think from a programmers perspective. What is stored in thetorch.Tensor objects we were creating above? Obviously the data and theshape, and maybe a few other things. But when we added two tensorstogether, we got an output tensor. All this output tensor knows is itsdata and shape. It has no idea that it was the sum of two other tensors(it could have been read in from a file, it could be the result of someother operation, etc.)If ``requires_grad=True``, the Tensor object keeps track of how it wascreated. Lets see it in action. | # Tensor factory methods have a ``requires_grad`` flag
x = torch.tensor([1., 2., 3], requires_grad=True)
# With requires_grad=True, you can still do all the operations you previously
# could
y = torch.tensor([4., 5., 6], requires_grad=True)
z = x + y
print(z)
# BUT z knows something extra.
print(z.grad_fn) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
So Tensors know what created them. z knows that it wasn't read in froma file, it wasn't the result of a multiplication or exponential orwhatever. And if you keep following z.grad_fn, you will find yourself atx and y.But how does that help us compute a gradient? | # Lets sum up all the entries in z
s = z.sum()
print(s)
print(s.grad_fn) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
So now, what is the derivative of this sum with respect to the firstcomponent of x? In math, we want\begin{align}\frac{\partial s}{\partial x_0}\end{align}Well, s knows that it was created as a sum of the tensor z. z knowsthat it was the sum x + y. So\begin{align}s = \overbrace{x_0 + y_0}^\text{$z_0$} + \overbrace{x_1 + y_1}^\text{$z_1$} + \overbrace{x_2 + y_2}^\text{$z_2$}\end{align}And so s contains enough information to determine that the derivativewe want is 1!Of course this glosses over the challenge of how to actually computethat derivative. The point here is that s is carrying along enoughinformation that it is possible to compute it. In reality, thedevelopers of Pytorch program the sum() and + operations to know how tocompute their gradients, and run the back propagation algorithm. Anin-depth discussion of that algorithm is beyond the scope of thistutorial. Lets have Pytorch compute the gradient, and see that we were right:(note if you run this block multiple times, the gradient will increment.That is because Pytorch *accumulates* the gradient into the .gradproperty, since for many models this is very convenient.) | # calling .backward() on any variable will run backprop, starting from it.
s.backward()
print(x.grad) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Understanding what is going on in the block below is crucial for being asuccessful programmer in deep learning. | x = torch.randn(2, 2)
y = torch.randn(2, 2)
# By default, user created Tensors have ``requires_grad=False``
print(x.requires_grad, y.requires_grad)
z = x + y
# So you can't backprop through z
print(z.grad_fn)
# ``.requires_grad_( ... )`` changes an existing Tensor's ``requires_grad``
# flag in-place. The input flag defaults to ``True`` if not given.
x = x.requires_grad_()
y = y.requires_grad_()
# z contains enough information to compute gradients, as we saw above
z = x + y
print(z.grad_fn)
# If any input to an operation has ``requires_grad=True``, so will the output
print(z.requires_grad)
# Now z has the computation history that relates itself to x and y
# Can we just take its values, and **detach** it from its history?
new_z = z.detach()
# ... does new_z have information to backprop to x and y?
# NO!
print(new_z.grad_fn)
# And how could it? ``z.detach()`` returns a tensor that shares the same storage
# as ``z``, but with the computation history forgotten. It doesn't know anything
# about how it was computed.
# In essence, we have broken the Tensor away from its past history | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
You can also stop autograd from tracking history on Tensorswith ``.requires_grad``=True by wrapping the code block in``with torch.no_grad():`` | print(x.requires_grad)
print((x ** 2).requires_grad)
with torch.no_grad():
print((x ** 2).requires_grad) | _____no_output_____ | BSD-3-Clause | docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Feature Engenering | SUFFIX_CAT = '__cat'
for feat in df.columns:
if isinstance( df[feat][0], list): continue
factorized_values = df[feat].factorize()[0]
if SUFFIX_CAT in feat:
df[feat] = factorized_values
else:
df[feat + SUFFIX_CAT] = df[feat].factorize()[0]
cat_feats = [x for x in df.columns if SUFFIX_CAT in x]
cat_feats = [x for x in cat_feats if 'price' not in x]
len(cat_feats)
def run_model(model, feats):
X = df[feats].values
y = df['price_value'].values
scores = cross_val_score(model, X, y, cv=3, scoring='neg_mean_absolute_error')
return np.mean(scores), np.std(scores) | _____no_output_____ | MIT | day4.ipynb | mszzukowski/dw_matrix_car |
DecisionTree | run_model(DecisionTreeRegressor(max_depth=5), cat_feats) | _____no_output_____ | MIT | day4.ipynb | mszzukowski/dw_matrix_car |
Random Forest | model = RandomForestRegressor(max_depth=5, n_estimators=50, random_state=0)
run_model(model=model, feats=cat_feats) | _____no_output_____ | MIT | day4.ipynb | mszzukowski/dw_matrix_car |
XGBoost | xgb_params = {
'max_depth': 5,
'n_estimators': 50,
'learning_rate': 0.1,
'seed': 0
}
run_model(xgb.XGBRegressor(**xgb_params), cat_feats)
m = xgb.XGBRegressor(**xgb_params)
m.fit(X,y)
imp = PermutationImportance(m, random_state=0).fit(X,y)
eli5.show_weights(imp, feature_names=cat_feats)
feats = [
'param_napęd__cat',
'param_rok-produkcji',
'param_stan__cat',
'param_skrzynia-biegów__cat',
'param_faktura-vat__cat',
'param_moc',
'param_marka-pojazdu__cat',
'feature_kamera-cofania__cat',
'param_typ__cat',
'param_pojemność-skokowa',
'seller_name__cat',
'feature_wspomaganie-kierownicy__cat',
'param_model-pojazdu__cat',
'param_wersja__cat',
'param_kod-silnika__cat',
'feature_system-start-stop__cat',
'feature_asystent-pasa-ruchu__cat',
'feature_czujniki-parkowania-przednie__cat',
'feature_łopatki-zmiany-biegów__cat',
'feature_regulowane-zawieszenie__cat',
]
run_model(xgb.XGBRegressor(**xgb_params), feats)
df['param_rok-produkcji'] = df['param_rok-produkcji'].map(lambda x : -1 if str(x) == 'None' else int(x))
df['param_moc'] = df['param_moc'].map(lambda x: -1 if str(x) == 'None' else x.split(' ')[0])
df['param_pojemność-skokowa'].unique()
df['param_pojemność-skokowa'] = df['param_pojemność-skokowa'].map(lambda x: -1 if str(x) == 'None' else x.split('cm')[0].replace(' ','')) | _____no_output_____ | MIT | day4.ipynb | mszzukowski/dw_matrix_car |
Riddler Battle Royale> [538's *The Riddler* Asks](http://fivethirtyeight.com/features/the-battle-for-riddler-nation-round-2/): *In a distant, war-torn land, there are 10 castles. There are two warlords: you and your archenemy, with whom you’re competing to collect the most victory points. Each castle has its own strategic value for a would-be conqueror. Specifically, the castles are worth 1, 2, 3, …, 9, and 10 victory points. You and your enemy each have 100 soldiers to distribute, any way you like, to fight at any of the 10 castles. Whoever sends more soldiers to a given castle conquers that castle and wins its victory points. If you each send the same number of troops, you split the points. You don’t know what distribution of forces your enemy has chosen until the battles begin. Whoever wins the most points wins the war. Submit a plan distributing your 100 soldiers among the 10 castles.* | # Load some useful modules
%matplotlib inline
import matplotlib.pyplot as plt
import csv
import random
from collections import Counter
from statistics import mean | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Let's play with this and see if we can find a good solution. Some implementation choices:* A `Plan` will be a tuple of 10 soldier counts (one for each castle).* `castles` will hold the indexes of the castles. Note that index 0 is castle 1 (worth 1 point) and index 9 is castle 10 (worth 10 points).* `half` is half the total number of points; if you get more than this you win.* `plans` will hold a set of plans that were submitted in the previous contest.* `play(A, B)` gives the single game reward for Plan A against Plan B: 1 if A wins, 0 if A loses, and 1/2 for a tie.* `reward(a, b, payoff)` returns payoff, payoff/2, or 0, depending on whether `a` is bigger than `b`. | Plan = tuple
castles = range(10)
half = 55/2
plans = {Plan(map(int, row[:10]))
for row in csv.reader(open('battle_royale.csv'))}
def play(A, B):
"Play Plan A against Plan B and return a reward (0, 1/2, or 1)."
A_points = sum(reward(A[c], B[c], c + 1) for c in castles)
return reward(A_points, half)
def reward(a, b, payoff=1): return (payoff if a > b else payoff / 2 if a == b else 0) | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
Some tests: | assert reward(6, 5, 9) == 9 # 6 soldiers defeat 5, winning all 9 of the castle's points
assert reward(6, 6, 8) == 4 # A tie on an 8-point castle is worth 4 points
assert reward(6, 7, 7) == 0 # No points for a loss
assert reward(30, 25) == 1 # 30 victory points beats 25
assert len(plans) == 1202
assert play((26, 5, 5, 5, 6, 7, 26, 0, 0, 0),
(25, 0, 0, 0, 0, 0, 0, 25, 25, 25)) == 1 # A wins game
assert play((26, 5, 5, 5, 6, 7, 26, 0, 0, 0),
(0, 25, 0, 0, 0, 0, 0, 25, 25, 25)) == 0 # B wins game
assert play((25, 5, 5, 5, 6, 7, 26, 0, 0, 0),
(25, 0, 0, 0, 0, 0, 0, 25, 25, 25)) == 1/2 # Tie game | _____no_output_____ | MIT | ipynb/Riddler Battle Royale.ipynb | mikiec84/pytudes |
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