InstaDeepAI
/

MOJO

@@ -1,199 +1,82 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+  - bulk RNA-seq
+  - DNA methylation
+  - biology
+  - transcriptomics
+  - epigenomics
+  - multimodal
 ---
+# MOJO
+MOJO (MultiOmics JOint representation learning) is a model that learns joint representations of bulk RNA-seq and DNA methylation through bimodal masked language modeling and is tailored for cancer-type classification and survival analysis on the TCGA dataset.
+**Developed by:** [InstaDeep](https://huggingface.co/InstaDeepAI)
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- [**Repository**](https://github.com/instadeepai/multiomics-open-research)
+- **Paper:** [Bimodal masked language modeling for bulk RNA-seq and DNA methylation
+representation learning]()
+### How to use
+Until its next release, the transformers library needs to be installed from source with
+the following command in order to use the models.
+PyTorch should also be installed.
+```
+pip install --upgrade git+https://github.com/huggingface/transformers.git
+pip install torch
+```
+## Other notes
+We also provide the params for the MOJO jax model in `jax_params`.
+A small snippet of code is provided below to run inference with the model using bulk RNA-seq and DNA methylation samples from the [TCGA](https://portal.gdc.cancer.gov/) dataset.
+```
+import numpy as np
+import pandas as pd
+from transformers import AutoModel, AutoTokenizer
+from huggingface_hub import hf_hub_download
+tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/MOJO", trust_remote_code=True)
+model = AutoModel.from_pretrained(
+    "InstaDeepAI/MOJO",
+    trust_remote_code=True,
+)
+n_examples = 4
+omic_dict = {}
+for omic in ["rnaseq", "methylation"]:
+    csv_path = hf_hub_download(
+        repo_id="InstaDeepAI/MOJO",
+        filename=f"data/tcga_{omic}_sample.csv",
+        repo_type="model",
+    )
+    omic_array = pd.read_csv(csv_path).drop(["identifier", "cohort"], axis=1).to_numpy()[:n_examples, :]
+    if omic == "rnaseq":
+        omic_array = np.log10(1 + omic_array)
+    assert omic_array.shape[1] == model.config.sequence_length
+    omic_dict[omic] = omic_array
+omic_ids = {
+    omic: tokens["input_ids"]
+    for omic, tokens in tokenizer.batch_encode_plus(omic_dict, pad_to_fixed_length=True, return_tensors="pt").items()
+}
+omic_mean_embeddings = model(omic_ids)["after_transformer_embedding"].mean(axis=1) # embeddings can be used for downstream tasks.
+```
+### Citing our work
+```
+```