| --- |
| license: cc-by-4.0 |
| pretty_name: vep_pathogenic_coding |
| task_categories: |
| - tabular-classification |
| tags: |
| - tabular |
| - biomedical |
| - benchmark |
| - TabBioMed |
| - genomics |
| - binary |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| - split: validation |
| path: data/val.parquet |
| - split: test |
| path: data/test.parquet |
| --- |
| |
| # vep_pathogenic_coding |
|
|
| Part of **TabBioMed** (*TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning*), a benchmark of 94 curated biomedical |
| tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed. |
|
|
| Classify coding single-nucleotide variants as pathogenic (ClinVar, label 1) or common (gnomAD MAF > 5%, label 0) from DNA sequence context only. Variants are strand-canonicalised (reverse-complemented when the reference allele is a purine, so it is always C or T). Features: GRCh38 reference bases at offsets -32..+32 around the variant (the central base is the reference allele) and the alternative allele (66 categorical), plus canonical 3-mer frequencies (k-mer and reverse complement merged) of the 513-bp reference window centred on the variant (32 numeric). Allele-frequency fields, annotation fields (VEP consequence, ClinVar IDs/review status) and genomic coordinates are excluded because they define or reveal the label. chrX/chrY variants (all pathogenic by construction) and two variants listed with both labels are removed. Split by whole chromosomes: test = chr1, chr4, chr21; validation = chr5, chr6, chr11; train = remaining autosomes. |
|
|
| | Domain | Task | Samples | Features | Classes | |
| |---|---|---|---|---| |
| | Genomics | binary | 37,166 | 98 | 2 | |
|
|
| **Splits.** Fixed train/val/test split (26056/5766/5344 rows). |
|
|
| ## Files |
|
|
| - `data/**.parquet`: one row per sample. `row_idx` is the row index in the original |
| TabBioMed array; `f_<i>[_<name>]` are the features (in order); `target` (or `target_<j>` |
| for multi-target tasks) is the label. Categorical feature indices (0-based, among the |
| features): `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]`. Missing values are stored as nulls. |
| - `metadata.json`: TabBioMed task metadata (task type, categorical indices, source, description). |
| - `feature_names.json`: original feature names, in column order. |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("TabBioMed/vep_pathogenic_coding") |
| ``` |
|
|
| A Croissant (1.1) description with Responsible-AI fields is distributed with the |
| TabBioMed release. |
|
|
| ## Source and license |
|
|
| Source: InstaDeepAI/genomics-long-range-benchmark (Hugging Face), task variant_effect_pathogenic (ClinVar, coding SNVs): ClinVar pathogenic variants vs gnomAD v3.1.2 common variants (MAF > 5%, AN >= 25,000) (Kao et al., 2024). Sequence context from the UCSC hg38 (GRCh38) reference genome. |
|
|
| This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also |
| cite and respect the terms of the original source. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{tabbiomed2026, |
| title = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning}, |
| author = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026} |
| } |
| ``` |
|
|
