Datasets:
File size: 3,777 Bytes
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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}
}
```
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