|
--- |
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dataset_info: |
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features: |
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- name: seq |
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dtype: string |
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- name: label |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 3068946 |
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num_examples: 53614 |
|
- name: valid |
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num_bytes: 155744 |
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num_examples: 2512 |
|
- name: test |
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num_bytes: 709292 |
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num_examples: 12851 |
|
download_size: 2058102 |
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dataset_size: 3933982 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: valid |
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path: data/valid-* |
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- split: test |
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path: data/test-* |
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license: apache-2.0 |
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task_categories: |
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- token-classification |
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tags: |
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- chemistry |
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- biology |
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size_categories: |
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- 10K<n<100K |
|
--- |
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|
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# Dataset Card for Stability Stability Dataset |
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### Dataset Summary |
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The Stability Stability task is to predict the concentration of protease at which a protein can retain its folded state. Protease, being integral to numerous biological processes, bears significant relevance and a profound comprehension of protein stability during protease interaction can offer immense value, especially in the creation of novel therapeutics. |
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## Dataset Structure |
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### Data Instances |
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For each instance, there is a string representing the protein sequence and a float number indicating the stability score. See the [stability prediction dataset viewer](https://huggingface.co/datasets/Bo1015/stability_prediction/viewer) to explore more examples. |
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``` |
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{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
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'label':0.17} |
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``` |
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The average for the `seq` and the `label` are provided below: |
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| Feature | Mean Count | |
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| ---------- | ---------------- | |
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| seq | 45 | |
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| label | 0.34 | |
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### Data Fields |
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- `seq`: a string containing the protein sequence |
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- `label`: a float number indicating the stability score of each sequence. |
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### Data Splits |
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The stability stability dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset. |
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| Dataset Split | Number of Instances in Split | |
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| ------------- | ------------------------------------------- | |
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| Train | 53,614 | |
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| Valid | 2,512 | |
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| Test | 12,851 | |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The dataset applied in this task is initially sourced from [Rocklin et al](https://www.science.org/doi/10.1126/science.aan0693) and subsequently collected within the [TAPE](https://github.com/songlab-cal/tape). |
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### Licensing Information |
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The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). |
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### Citation |
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If you find our work useful, please consider citing the following paper: |
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``` |
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@misc{chen2024xtrimopglm, |
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title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein}, |
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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}, |
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year={2024}, |
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eprint={2401.06199}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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note={arXiv preprint arXiv:2401.06199} |
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} |
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``` |
