| | --- |
| | dataset_info: |
| | features: |
| | - name: seqs |
| | dtype: string |
| | - name: labels |
| | dtype: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 88951983 |
| | num_examples: 283057 |
| | - name: valid |
| | num_bytes: 19213838 |
| | num_examples: 62973 |
| | - name: test |
| | num_bytes: 22317993 |
| | num_examples: 73205 |
| | download_size: 127755417 |
| | dataset_size: 130483814 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | - split: valid |
| | path: data/valid-* |
| | - split: test |
| | path: data/test-* |
| | --- |
| | |
| | # INFORMATION FROM [HERE](https://huggingface.co/datasets/biomap-research/temperature_stability) PLEASE CITE THEIR PAPER BELOW |
| |
|
| | ### Dataset Summary |
| |
|
| | The accurate prediction of protein thermal stability has far-reaching implications in both academic and industrial spheres. This task primarily aims to predict a protein’s capacity to preserve its structural stability under a temperature condition of 65 degrees Celsius. |
| |
|
| | ## Dataset Structure |
| |
|
| | ### Data Instances |
| | For each instance, there is a string representing the protein sequence and an integer label indicating whether the protein can maintain its structural stability at a temperature of 65 degrees Celsius. See the [temperature stability dataset viewer](https://huggingface.co/datasets/Bo1015/temperature_stability/viewer) to explore more examples. |
| |
|
| | ``` |
| | {'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
| | 'label':1} |
| | ``` |
| |
|
| | The average for the `seq` and the `label` are provided below: |
| |
|
| | | Feature | Mean Count | |
| | | ---------- | ---------------- | |
| | | seq | 300 | |
| |
|
| |
|
| | ### Data Fields |
| |
|
| | - `seq`: a string containing the protein sequence |
| | - `label`: an integer label indicating the structural stability of each sequence. |
| |
|
| | ### Data Splits |
| |
|
| | The temperature stability dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset. |
| |
|
| | | Dataset Split | Number of Instances in Split | |
| | | ------------- | ------------------------------------------- | |
| | | Train | 283,057 | |
| | | Valid | 62,973 | |
| | | Test | 73,205 | |
| |
|
| | ### Source Data |
| |
|
| | #### Initial Data Collection and Normalization |
| |
|
| | We adapted the dataset strategy from [TemStaPro](https://academic.oup.com/bioinformatics/article/40/4/btae157/7632735). |
| |
|
| | ### 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} |
| | } |
| | ``` |
