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@@ -21,4 +21,84 @@ configs:
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  path: data/train-*
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  - split: test
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  path: data/test-*
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+
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+ # Dataset Card for Secondary Structure Prediction (Q8) Dataset
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+
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+ ### Dataset Summary
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+
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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 - Alpha-helix, G - 3-10 helix, I - Pi helix, E - Beta-strand, B - Beta-bridge, T - Turn, S - Bend, C - Coil (or random coil).
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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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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+ ```
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+ {'seq':'GKITFYEDRGFQGRHYECSSDHSNLQPYFSRCNSIRVDSGCWMLYEQPNFQGPQYFLRRGDYPDYQQWMGLNDSIRSCRLIPHTGSHRLRIYEREDYRGQMVEITEDCSSLHDRFHFSEIHSFNVLEGWWVLYEMTNYRGRQYLLRPGDYRRYHDWGATNARVGSLRRAVDFY'
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+ 'label':[ 7, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 7, 6, 6, 6, 7, 5, 7, 7, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 5, 5, 6, 6, 6, 6, 6, 7, 5, 5, 5, 7, 7, 7, 4, 4, 4, 4, 4, 7, 7, 7, 5, 7, 7, 4, 4, 4, 4, 4, 5, 5, 0, 0, 0, 7, 5, 7, 4, 4, 4, 4, 7, 5, 7, 3, 5, 7, 5, 6, 6, 6, 5, 5, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 5, 7, 4, 4, 4, 4, 5, 5, 5, 5, 5, 7, 5, 7, 4, 4, 4, 4, 7, 5, 4, 4, 4, 7, 5, 0, 0, 0, 0, 6, 7, 5, 5, 7, 7, 7, 7, 4, 4, 4, 4, 7, 7, 7, 7, 7 ]}
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+ ```
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+
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+ The average for the `seq` and the `label` are provided below:
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+
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+ | Feature | Mean Count |
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+ | ---------- | ---------------- |
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+ | seq | 256 |
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+ | label (0) | 9 |
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+ | label (1) | 82 |
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+ | label (2) | 1 |
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+ | label (3) | 3 |
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+ | label (4) | 52 |
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+ | label (5) | 26 |
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+ | label (6) | 19 |
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+ | label (7) | 63 |
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+
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+ ### Data Fields
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+
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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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+
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+ ### Data Splits
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+
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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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+
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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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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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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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+
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+
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+ ### Licensing Information
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+
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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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+
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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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+ ```
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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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+ ```