|
--- |
|
dataset_info: |
|
features: |
|
- name: seq |
|
dtype: string |
|
- name: label |
|
dtype: int64 |
|
splits: |
|
- name: train |
|
num_bytes: 19408437 |
|
num_examples: 62478 |
|
- name: test |
|
num_bytes: 2176357 |
|
num_examples: 6942 |
|
download_size: 21064069 |
|
dataset_size: 21584794 |
|
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 |
|
--- |
|
|
|
|
|
# Dataset Card for Solubility Prediction Dataset |
|
|
|
### Dataset Summary |
|
|
|
This solubility prediction task involves a binary classification of a heterogenous set of proteins, assessing them as either soluble or insoluble. The solubility metric is a crucial design parameter in ensuring protein efficacy, with particular relevance in the pharmaceutical domain. |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
For each instance, there is a string representing the protein sequence and an integer label indicating that the protein sequence is soluble or insoluble. See the [solubility prediction dataset viewer](https://huggingface.co/datasets/Bo1015/solubility_prediction/viewer) to explore more examples. |
|
|
|
``` |
|
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
|
'label':1} |
|
``` |
|
|
|
The average for the `seq` and the `label` are provided below: |
|
|
|
| Feature | Mean Count | |
|
| ---------- | ---------------- | |
|
| seq | 298 | |
|
| label (0) | 0.58 | |
|
| label (1) | 0.42 | |
|
|
|
|
|
|
|
|
|
### Data Fields |
|
|
|
- `seq`: a string containing the protein sequence |
|
- `label`: an integer label indicating that the protein sequence is soluble or insoluble. |
|
|
|
### Data Splits |
|
|
|
The solubility prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset. |
|
|
|
| Dataset Split | Number of Instances in Split | |
|
| ------------- | ------------------------------------------- | |
|
| Train | 62,478 | |
|
| Test | 6,942 | |
|
|
|
### Source Data |
|
|
|
#### Initial Data Collection and Normalization |
|
|
|
The initialized dataset is adapted from [DeepSol](https://academic.oup.com/bioinformatics/article/34/15/2605/4938490). Within this framework, any protein exhibiting a sequence identity of 30% or greater to any protein within the test subset is eliminated from both the training subsets, ensuring robust and unbiased evaluation. |
|
|
|
### 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} |
|
} |
|
``` |
