Datasets:

TabBioMed
/

vep_pathogenic_coding

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.

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

@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}
}