Datasets:
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---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 3669447
num_examples: 6622
- name: test
num_bytes: 1038554
num_examples: 1842
download_size: 4646324
dataset_size: 4708001
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- text-classification
tags:
- chemistry
- biology
- medical
size_categories:
- 1K<n<10K
---
# Dataset Card for Localization Prediction Dataset
### Dataset Summary
The task of Protein Subcellular Localization Prediction bears substantial relevance in bioinformatics, owing to its contributions to proteomics research and its potential to augment our comprehension of protein function and disease mechanisms. In this task, the input to the model is an amino acid sequence of a protein, which is transformed into an output comprising a probability distribution over 10 unique subcellular localization categories.
## Dataset Structure
### Data Instances
For each instance, there is a string representing the protein sequence and an integer label indicating which subcellular position the protein sequence locates at. See the [localization prediction dataset viewer](https://huggingface.co/datasets/Bo1015/localization_prediction/viewer) to explore more examples.
```
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':6}
```
The average for the `seq` and the `label` are provided below:
| Feature | Mean Count |
| ---------- | ---------------- |
| seq | 544 |
| label (0) | 0.01 |
| label (1) | 0.10 |
| label (2) | 0.20 |
| label (3) | 0.03 |
| label (4) | 0.07 |
| label (5) | 0.06 |
| label (6) | 0.11 |
| label (7) | 0.34 |
| label (8) | 0.06 |
| label (9) | 0.02 |
### Data Fields
- `seq`: a string containing the protein sequence
- `label`: an integer label indicating which subcellular position the protein sequence locates at.
### Data Splits
The localization prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 6,622 |
| Test | 1,842 |
### Source Data
#### Initial Data Collection and Normalization
The dataset applied for this task is derived from Uniprot, meticulously curated by [DeepLoc](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857).
### Licensing Information
The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation
If you find our work useful, please consider citing the following paper:
```
@misc{chen2024xtrimopglm,
title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
year={2024},
eprint={2401.06199},
archivePrefix={arXiv},
primaryClass={cs.CL},
note={arXiv preprint arXiv:2401.06199}
}
``` |