Datasets:

TabBioMed
/

continental_50k_missing

license: cc-by-4.0
pretty_name: continental_50k_missing
task_categories:
  - tabular-classification
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - genomics
  - classification
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: validation
        path: data/val.parquet
      - split: test
        path: data/test.parquet

continental_50k_missing

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 chr22 for a subset of samples from the 1000 Genomes Project and HGDP. For this specific dataset, 50k SNPs at random positions were selected from the original sequences and missing values where introduced with a probability of 60%. The task is to predict the continental ancestry of each sample based on the genotypes.

Domain	Task	Samples	Features	Classes
Genomics	classification	3,309	25,921	7

Splits. Fixed train/val/test split (2317/496/496 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).

from datasets import load_dataset
ds = load_dataset("TabBioMed/continental_50k_missing")

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. Continental labels (Super-Populations: EUR, AFR, EAS, SAS, AMR) were obtained from the harmonized gnomAD v3.1 sample metadata.

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}
}