README.md

metadata

license: cc-by-4.0
pretty_name: proteinea_solubility
task_categories:
  - tabular-classification
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - proteomics
  - binary
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: validation
        path: data/val.parquet
      - split: test
        path: data/test.parquet

proteinea_solubility

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.

Sample-level binary classification task predicting protein solubility. Labels: 1 = soluble, 0 = insoluble. Features are k-mer (k=3) frequency vectors computed from single amino-acid sequences. Missing feature values are not imputed.

Domain	Task	Samples	Features	Classes
Proteomics	binary	71,421	8,000	2

Splits. Fixed train/val/test split (62478/6942/2001 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/proteinea_solubility")

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

Source and license

Source: proteinea/solubility (Hugging Face). Binary protein solubility prediction dataset. Labels indicate whether a protein is soluble (1) or insoluble (0). Separate train, validation and test CSV files.

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}
}