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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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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 3238298 |
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num_examples: 57357 |
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- name: valid |
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num_bytes: 395504 |
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num_examples: 7008 |
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- name: test |
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num_bytes: 474618 |
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num_examples: 8406 |
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download_size: 1494430 |
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dataset_size: 4108420 |
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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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- text-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 Peptide-HLA/MHC Affinity Dataset |
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### Dataset Summary |
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The human leukocyte antigen (HLA) gene encodes major histo-compatibility complex (MHC) proteins, which can bind to peptide fragments and be presented to the cell surface for subsequent T cell receptors (TCRs) recognition. Accurately predicting the interaction between peptide sequence and HLA molecule will boost the understanding of immune responses, antigen presentation, and designing therapeutic interventions such as peptide-based vaccines or immunotherapies. |
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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 an integer label indicating that whether a given paired peptide and HLA sequence can bind or not. See the [peptide-HLA/MHC affinity dataset viewer](https://huggingface.co/datasets/Bo1015/peptide_HLA_MHC_affinity/viewer) to explore more examples. |
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``` |
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{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL' |
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'label':1} |
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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) | 0.5 | |
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| label (1) | 0.5 | |
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### Data Fields |
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- `seq`: a string containing the protein sequence |
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- `label`: an integer label indicating that whether a given paired peptide and HLA sequence can bind or not. |
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### Data Splits |
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The Peptide-HLA/MHC Affinity 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 | 57,357 | |
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| Valid | 7,008 | |
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| Test | 8,406 | |
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### Source Data |
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#### Initial Data Collection and Normalization |
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The modeling data is from [Wu et al](https://www.biorxiv.org/content/10.1101/2023.04.24.538196v1). The raw dataset contains millions of samples, we used the same split and downsample 1% for training and 5% for validation and testing (57,357 training samples, 7,008 validation samples and 8,406 test samples). |
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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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``` |
