---
pretty_name: avida_htnfa_3mer
task_categories:
- tabular-classification
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- proteomics
- binary
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/val.parquet
  - split: test
    path: data/test.parquet
license: cc-by-nc-4.0
---

# avida_htnfa_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 hTNFa. Single antigen — antigen features omitted. Features: TF-IDF of amino-acid 3-mers on VHH sequence only (vocabulary and IDF fitted on training rows). Split: CD-HIT families (identity 0.8, coverage 0.8, on the VHH core without the constant leader and His-tag) assigned to train/val/test 70/15/15 by a label-balanced local search (partition sizes and val/test positive rates matched to the overall data); VHH families never span splits.

| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Proteomics | binary | 5,580 | 5,000 | 2 |

**Splits.** Fixed train/val/test split (3910/835/835 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).


```python
from datasets import load_dataset
ds = load_dataset("TabBioMed/avida_htnfa_3mer")
```

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

## Source and license

Dataset license: [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/).

- [COGNANO/AVIDa-hTNFa](https://huggingface.co/datasets/COGNANO/AVIDa-hTNFa). COGNANO, AVIDa-hTNFa. Source license: [CC-BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/). Retain attribution and the license link, identify modifications, and do not imply endorsement. Use is restricted to noncommercial purposes.

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

## Citation

```bibtex
@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}
}
```