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license: mit |
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## This uploaded dataset is a sample data of 12k rows for train and 1.2k for test to get idea of the dataset used in making of PathoPreter |
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<a href="https://huggingface.co/YADAV0206/PathoPreter-4B-SNV-Pathogen-ClinVar-gnomAD">https://huggingface.co/YADAV0206/PathoPreter-4B-SNV-Pathogen-ClinVar-gnomAD</a> |
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The model's full training dataset contains approximately 144k pathogenic variants and 1.05 million benign variants, for a total of about 1.2 million samples and Testing have 55k total different samples with 11 seperate ablations tests (on same 55k rows) making around 12*55k=660k” |
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To get the Dataset contact |
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**Rohit Yadav** |
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<div> |
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<a href="yrohit1825@gmail.com">yrohit1825@gmail.com</a> | <a href="https://github.com/YADAV1825/PathoPreter">Github: https://github.com/YADAV1825/PathoPreter</a> |
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</div> |
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--- |
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## 📦 Dataset Availability |
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<div>Dataset Construction and Availability:</div> |
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The datasets used to train and evaluate PathoPreter (including large-scale ClinVar-derived SNV corpora, controlled ablation test suites, and robustness evaluation datasets) were fully constructed in-house using publicly available, permissively licensed genomic resources such as ClinVar and gnomAD. All upstream sources are properly credited and explicitly permit commercial use and redistribution. |
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Significant original engineering and curation effort was applied beyond raw data usage. This included large-scale extraction, normalization, schema unification, quality control, deduplication, and strict train–test disjointness enforcement. Approximately 8 million raw ClinVar variants and ~250 GB of gnomAD VCF data were processed and merged into production-grade Parquet datasets optimized for large-scale analytics, machine learning training, and downstream integration. |
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The end-to-end data construction process required approximately 150 hours of compute time and over 250 hours of expert engineering and curation work. The resulting datasets constitute a high-value derived data asset, distinct from the original source distributions. |
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These datasets are available for licensed distribution to startups, enterprises, and research organizations for use in applied genomics, AI/ML model development, benchmarking, variant prioritization workflows, and internal research. Commercial licensing, redistribution terms, and support options are available upon request. |
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Available components include:(in Parquet and CSV both) |
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- Large-scale ClinVar-style SNV training dataset |
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- Held-out test set with identical variants across ablations |
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- Controlled ablation datasets (signal removal studies) |
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- Fake-variant's robustness evaluation dataset (see below why is it important in FAKE VARIANT ROBUSTNESS TEST) |
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- Balanced CSV subsets suitable for classical ML training |
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- Data audit and leakage-verification scripts |
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If you are interested in: |
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- dataset licensing |
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- research or industry use |
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- collaboration or benchmarking |
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- reproducing or extending this work |
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please contact: |
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**Rohit Yadav** |
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<div> |
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<a href="yrohit1825@gmail.com">yrohit1825@gmail.com</a> | <a href="https://github.com/YADAV1825/PathoPreter">Github: https://github.com/YADAV1825/PathoPreter</a> |
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</div> |
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Requests are evaluated on a case-by-case basis. |
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