Datasets:
| license: cc-by-4.0 | |
| pretty_name: dna | |
| task_categories: | |
| - tabular-classification | |
| tags: | |
| - tabular | |
| - biomedical | |
| - benchmark | |
| - TabBioMed | |
| - genomics | |
| - classification | |
| 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 | |
| # dna | |
| 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. | |
| Talent Benchmark dataset: dna (task=classification). Exact duplicate sequences with the same label keep one row. One sequence with conflicting labels is removed. TabBioMed then applies a 70/15/15 train/val/test split (stratified, random_state=42), not the original Talent split boundaries. | |
| | Domain | Task | Samples | Features | Classes | | |
| |---|---|---|---|---| | |
| | Genomics | classification | 3,000 | 180 | 3 | | |
| **Splits.** Fixed train/val/test split (2100/450/450 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): `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179]`. 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/dna") | |
| ``` | |
| A Croissant (1.1) description with Responsible-AI fields is distributed with the | |
| TabBioMed release. | |
| ## Source and license | |
| Source: Talent Benchmark Dataset - dna | |
| 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 | |
| ```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} | |
| } | |
| ``` | |