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--- |
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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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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 24941535 |
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num_examples: 10848 |
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- name: test |
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num_bytes: 1665908 |
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num_examples: 667 |
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download_size: 3610640 |
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dataset_size: 26607443 |
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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: 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 Secondary Structure Prediction (Q3) Dataset |
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### Dataset Summary |
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The study of a protein’s secondary structure (Sec. Struc. P.) forms a fundamental cornerstone in understanding its biological function. This secondary structure, comprising helices, strands, and various turns, bestows the protein with a specific three-dimensional configuration, which is critical for the formation of its tertiary structure. In the context of this work, a given protein sequence is classified into three distinct categories, each representing a different structural element: H - Helix (includes alpha-helix, 3-10 helix, and pi helix), E - Strand (includes beta-strand and beta-bridge), C - Coil (includes turns, bends, and random coils). |
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## Dataset Structure |
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### Data Instances |
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For each instance, there is a string of the protein sequences, a sequence for the strucutral labels. See the [Secondary structure prediction dataset viewer](https://huggingface.co/datasets/Bo1015/ssp_q8/viewer/default/test) to explore more examples. |
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``` |
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{'seq':'MRGSHHHHHHGSVKVKFVSSGEEKEVDTSKIKKVWRNLTKYGTIVQFTYDDNGKTGRGYVRELDAPKELLDMLARAEGKLN' |
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'label':[ 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2 ]} |
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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 | 256 | |
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| label (0) | 109 | |
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| label (1) | 54 | |
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| label (2) | 92 | |
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### Data Fields |
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- `seq`: a string containing the protein sequence |
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- `label`: a sequence containing the structural label of each residue. |
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### Data Splits |
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The secondary structure prediction dataset has 2 splits: _train_ 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 | 10,848 | |
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| Test | 667 | |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The datasets applied in this study were originally published by [NetSurfP-2.0](https://pubmed.ncbi.nlm.nih.gov/30785653/). |
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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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``` |
