sexcheck_chrX_50k

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.

This dataset contains genotypes from chromosome X for a subset of samples from the 1000 Genomes Project and HGDP, along with labels indicating the sex of each sample. For this specific dataset, 50k SNPs where randomly selected from the original sequences and missing values where introduced with a probability of 0.95. The task is to predict the sex of each sample based on the genotypes.Due to the high missingess, each sample was duplicated three times with different missingness patterns. Three independently masked rows represent each of 4,026 individuals. A seed-42, sex-stratified 70/15/15 split assigns individuals before expanding to rows; every individual stays in one train/validation/test partition. Individual IDs are split metadata, never predictors. X/y and masks are preserved.

Domain	Task	Samples	Features	Classes
Genomics	binary	12,078	17,118	2

Splits. Fixed train/val/test split (8454/1812/1812 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): []. Missing values are stored as nulls.
• metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
• split_groups.json: groups[row_idx] gives the sample group (e.g. patient, donor or strain) that must stay together in any re-split. Split metadata only, never a feature.
• sample_metadata.csv: per-row provenance (identifiers, source annotations); not features.

from datasets import load_dataset
ds = load_dataset("TabBioMed/sexcheck_chrX_50k")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: Chromosome data was dowloaded from GnomAD v3.1.2 - 1KG + HGDP callset: https://gnomad.broadinstitute.org/downloads#v3-hgdp-1kg. Labels were obtained from 1KG and International Genome Sample Resource (IGSR) with the respective links: https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20250704.ALL.ped and https://www.internationalgenome.org/data-portal/data-collection/HGDP

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}
}