TOX_171

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.

Retrospective four-group classification of healthy controls, skin-cancer controls, limited-radiation-toxicity controls, and acute-radiation-toxicity cases. The 171 gene-expression profiles represent 57 donors, each measured in donor-derived lymphoblastoid cells under mock, ultraviolet, and ionizing-radiation conditions (GEO GDS968/GSE1725; Rieger et al., PNAS 2004). Five donor-grouped outer folds test every profile once; three donor-grouped inner folds select hyperparameters. The final-fit validation holdout is also donor-disjoint. Donor and GEO sample identifiers are split metadata, not features.

Domain	Task	Samples	Features	Classes
Transcriptomics	classification	171	5,748	4

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 117/18/36 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.

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/TOX_171")

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

Source and license

Source: GEO_GDS968_GSE1725

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}
}