| --- |
| license: cc-by-4.0 |
| pretty_name: PT-Mush21 |
| task_categories: |
| - tabular-classification |
| tags: |
| - tabular |
| - biomedical |
| - benchmark |
| - TabBioMed |
| - metabolomics |
| - classification |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: fold_0_train |
| path: data/fold_0/train.parquet |
| - split: fold_0_validation |
| path: data/fold_0/val.parquet |
| - split: fold_0_test |
| path: data/fold_0/test.parquet |
| - split: fold_1_train |
| path: data/fold_1/train.parquet |
| - split: fold_1_validation |
| path: data/fold_1/val.parquet |
| - split: fold_1_test |
| path: data/fold_1/test.parquet |
| - split: fold_2_train |
| path: data/fold_2/train.parquet |
| - split: fold_2_validation |
| path: data/fold_2/val.parquet |
| - split: fold_2_test |
| path: data/fold_2/test.parquet |
| - split: fold_3_train |
| path: data/fold_3/train.parquet |
| - split: fold_3_validation |
| path: data/fold_3/val.parquet |
| - split: fold_3_test |
| path: data/fold_3/test.parquet |
| - split: fold_4_train |
| path: data/fold_4/train.parquet |
| - split: fold_4_validation |
| path: data/fold_4/val.parquet |
| - split: fold_4_test |
| path: data/fold_4/test.parquet |
| --- |
| |
| # PT-Mush21 |
|
|
| 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. |
|
|
| PTR-ToF-MS fungal species classification: 593 measurements and 383 peaks from 25 biological strains of six species, measured in pure culture and co-culture with Pseudomonas helmanticensis (Probst et al., Scientific Reports 2023). Labels 0-5 correspond to Entomortierella galaxiae, Linnemannia exigua, L. hyalina, L. sclerotiella, L. gamsii and L. solitaria. Five strain-grouped outer folds test every measurement once. The three requested inner folds reduce to two in each outer fold because of limited class support at the strain level. The final-fit validation holdout is also strain-disjoint. Sample, strain and condition identifiers are metadata, not features. Source PTR-ToF-MS peak values are area-normalized to a row sum of one and are retained without an additional logarithmic transformation. Feature scaling is fitted within each training partition. |
|
|
| | Domain | Task | Samples | Features | Classes | |
| |---|---|---|---|---| |
| | Metabolomics | classification | 593 | 383 | 6 | |
|
|
| **Splits.** 5 stored outer cross-validation folds (`data/fold_<i>/`), each with train/val/test files (fold 0: 332/142/119 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). |
| - `split_groups.json`: `groups[row_idx]` gives the sample group (e.g. patient, donor or strain) that must stay together in any re-split. Split metadata only, never a feature. |
| - `sample_metadata.csv`: per-row provenance (identifiers, source annotations); not features. |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("TabBioMed/PT-Mush21") |
| ``` |
|
|
| A Croissant (1.1) description with Responsible-AI fields is distributed with the |
| TabBioMed release. |
|
|
| ## Source and license |
|
|
| Source: On the use of TabPFN on mass spectrometry analysis of volatile organic compounds. Scientific Reports (2025). GitHub: https://github.com/CIFASIS/TabPFN-VOCS |
|
|
| 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} |
| } |
| ``` |
|
|
