Datasets:
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---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 815082
num_examples: 1706
- name: test
num_bytes: 92795
num_examples: 190
download_size: 901099
dataset_size: 907877
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:
- biology
- chemistry
size_categories:
- 1K<n<10K
---
# Dataset Card for Optimal Temperature Dataset
### Dataset Summary
Grasping the catalytic activity of enzymes is pivotal for industrial enzyme design, particularly in predicting the optimal temperature for a given enzyme’s catalytic effect.
## Dataset Structure
### Data Instances
For each instance, there is a string representing the protein sequence and a float value indicating the optimal temperature for a given enzyme’s catalytic effect. See the [optimal temperature dataset viewer](https://huggingface.co/datasets/Bo1015/optimal_temperature/viewer) to explore more examples.
```
{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
'label':60.5}
```
The average for the `seq` and the `label` are provided below:
| Feature | Mean Count |
| ---------- | ---------------- |
| seq | 467 |
| label (0) | 50.0 |
### Data Fields
- `seq`: a string containing the protein sequence
- `label`: a float value indicating the optimal temperature for a given enzyme’s catalytic effect.
### Data Splits
The Optimal Temperature dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 1,706 |
| Test | 190 |
### Source Data
#### Initial Data Collection and Normalization
The dataset utilized for this task is primarily procured by [DeepET](https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4480), a recent advancement in the field that uses deep learning techniques to understand enzyme thermal adaptation.
### 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}
}
``` |