tahoe_g2m_score

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.

Single-cell RNA-seq data from the Tahoe-100M dataset (ARC Institute, 2025). Cells are subsampled; gene selection uses training cells only, preserving raw UMI counts. Task: reconstruct the source expression-derived G2/M score (regression). Method: pearson_residuals (genes selected on training cells), Dim: 5000.

Domain	Task	Samples	Features	Classes
Single-cell	regression	21,426	5,000	-

Splits. Fixed train/val/test split (15000/3213/3213 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/tahoe_g2m_score")

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

Source and license

Dataset license: CC0-1.0.

• tahoebio/Tahoe-100M. Tahoe Bio and Arc Institute, Tahoe-100M (2025), DOI 10.1101/2025.02.20.639398. Source license: CC0-1.0. CC0 applies to the source data. Source attribution is requested for scientific provenance. Third-party trademarks are not licensed.

See NOTICE.md for attribution and modifications and LICENSES/ for license texts.

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}
}