cptac_histology

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.

Binary classification of endometrioid (0) versus serous (1) carcinoma from CPTAC UCEC tumour proteomics (umich), aligned to MSSM baseline histological annotations. One tumour profile per patient; clear-cell, mixed and unrecorded histologies are excluded. The release contains 101 patients: 80 endometrioid and 21 serous. Normal-tissue profiles and clinical annotations are excluded from the features. Missing protein measurements remain NaN; the general pipeline removes constant/all-empty columns and fits imputation/scaling within training partitions. Five stratified outer folds use three-fold inner CV for hyperparameter selection.

Domain	Task	Samples	Features	Classes
Proteomics	binary	101	12,592	2

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 70/10/21 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).
• feature_names.json: original feature names, in column order.

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

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

Source and license

Source: CPTAC (Clinical Proteomic Tumor Analysis Consortium). Proteomics data from the UCEC (Uterine Corpus Endometrial Carcinoma) cohort, measured at University of Michigan (umich). Clinical labels from the MSSM (Icahn School of Medicine at Mount Sinai) annotation.

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}
}