Datasets:

TabBioMed
/

pca_1k

license: cc-by-4.0
pretty_name: pca_1k
task_categories:
  - tabular-regression
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - genomics
  - multiregression
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

pca_1k

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, 1000 SNPs at random positions were selected from the original sequences and missing values where introduced with a probability of 0%. The task is to predict the first 3 PCA coordinates of each sample based on the genotypes.

Domain	Task	Samples	Features	Classes
Genomics	multiregression	2,609	707	-

Splits. Fixed train/val/test split (1827/391/391 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/pca_1k")

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

Source and license

Source: Chromosome data was downloaded from GnomAD v3.1.2 - 1KG + HGDP callset: https://gnomad.broadinstitute.org/downloads#v3-hgdp-1kg. PCA coordinates were computed utilizing the 300,000 most informative SNPs from chromosomes chr1-chr22.

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}
}