Commit
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Parent(s):
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Update parquet files
Browse files- .gitattributes +0 -37
- README.md +0 -46
- data/train-00000-of-00001.parquet → damlab--HIV_PI/parquet-train.parquet +2 -2
- dataset_infos.json +0 -1
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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# Audio files - uncompressed
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README.md
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---
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license: mit
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---
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# Dataset Description
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## Dataset Summary
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This dataset was derived from the Stanford HIV Genotype-Phenotype database and contains 1,733 HIV protease sequences. A
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pproximately half of the sequences are resistant to at least one antiretroviral therapeutic (ART).
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Supported Tasks and Leaderboards: None
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Languages: English
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## Dataset Structure
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### Data Instances
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Each column represents the protein amino acid sequence of the HIV protease protein. The ID field indicates the Genbank reference ID for future cross-referencing. There are 1,733 total protease sequences.
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Data Fields: ID, sequence, fold, FPV, IDV, NFV, SQV
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Data Splits: None
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## Dataset Creation
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Curation Rationale: This dataset was curated to train a model (HIV-BERT-PI) designed to predict whether an HIV protease sequence would result in resistance to certain antiretroviral (ART) drugs.
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Initial Data Collection and Normalization: Dataset was downloaded and curated on 12/21/2021.
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## Considerations for Using the Data
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Social Impact of Dataset: Due to the tendency of HIV to mutate, drug resistance is a common issue when attempting to treat those infected with HIV.
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Protease inhibitors are a class of drugs that HIV is known to develop resistance via mutations.
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Thus, by providing a collection of protease sequences known to be resistant to one or more drugs, this dataset provides a significant collection of data that could be utilized to perform computational analysis of protease resistance mutations.
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Discussion of Biases: Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D.
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Currently, there was no effort made to balance the performance across these classes.
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As such, one should consider refinement with additional sequences to perform well on non-B sequences.
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## Additional Information:
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- Dataset Curators: Will Dampier
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- Citation Information: TBA
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data/train-00000-of-00001.parquet → damlab--HIV_PI/parquet-train.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8bfc66e7628bc6a50d105a748e2f800cfef364e95442fbae58ba5ad0d79071c
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size 72839
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dataset_infos.json
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{"damlab--HIV_PI": {"description": "This dataset was constructed the Stanford HIV Drug Resistance Database. \nhttps://hivdb.stanford.edu/pages/genopheno.dataset.html\nThe sequences were interpolated from the protease high-quality dataset.\nSequences with >4-fold increased resistance relative to wild-type was labeled as True.\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {HIV Protease Drug Resistance Prediction Dataset},\nauthor={Will Dampier\n},\nyear={2021}\n}\n", "homepage": "", "license": "", "features": {"sequence": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "FPV": {"dtype": "bool", "id": null, "_type": "Value"}, "IDV": {"dtype": "bool", "id": null, "_type": "Value"}, "NFV": {"dtype": "bool", "id": null, "_type": "Value"}, "SQV": {"dtype": "bool", "id": null, "_type": "Value"}, "fold": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": null, "config_name": null, "version": null, "splits": {"train": {"name": "train", "num_bytes": 205318, "num_examples": 1733, "dataset_name": "HIV_PI"}}, "download_checksums": null, "download_size": 69238, "post_processing_size": null, "dataset_size": 205318, "size_in_bytes": 274556}}
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