avida_sarscov2_3mer

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: predict VHH nanobody binding to 13 antigens (12 SARS-CoV-2 spike constructs plus HCoV-OC43 as a control) under 17 antigen labels: Alpha, Beta, Delta and Omicron were screened by both bead panning (purified ectodomain, '_bead') and cell panning (full-length membrane spike, '_cell'), and bead/cell constructs have different antigen sequences. Features: [TF-IDF(VHH 3-mers) | TF-IDF(Ag 3-mers)] hstacked (vocabularies and IDF fitted on training rows). Split: COGNANO official train/test, which splits by alpaca (Alpaca P train, Alpaca C test); validation (up to 7k samples) is carved from the Alpaca P training set as whole VHH families (CD-HIT identity 0.8 on the VHH core), so no validation VHH occurs in train. After subsampling, exact (VHH sequence, antigen sequence) pairs found in test are removed from training/validation without using labels; test samples are preserved.

Domain	Task	Samples	Features	Classes
Proteomics	binary	26,979	7,072	2

Splits. Fixed train/val/test split (9988/6991/10000 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/avida_sarscov2_3mer")

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

Source and license

Source: COGNANO AVIDa datasets (HuggingFace: cognano/AVIDa-hIL6, cognano/AVIDa-hTNFa, cognano/AVIDa-SARS-CoV-2). VHH (nanobody) sequences with binary binding labels against antigen variants.

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}
}