TabBioMed camera-ready release

README.md

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+---
+license: cc-by-4.0
+pretty_name: MTBLS136
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- biomedical
+- benchmark
+- TabBioMed
+- metabolomics
+- binary
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: validation
+    path: data/val.parquet
+  - split: test
+    path: data/test.parquet
+---
+
+# MTBLS136
+
+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.
+
+A serum LC-MS dataset consisting of 949 named metabolites. We compare estrogen plus progestin (E+P; Class=1) vs. estrogen-only (E; Class=0).
+
+| Domain | Task | Samples | Features | Classes |
+|---|---|---|---|---|
+| Metabolomics | binary | 668 | 915 | 2 |
+
+**Splits.** Fixed train/val/test split (468/100/100 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/MTBLS136")
+```
+
+A Croissant (1.1) description with Responsible-AI fields is distributed with the
+TabBioMed release.
+
+## Source and license
+
+Source: CIMBC/MetabComparisonBinaryML
+
+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}
+}
+```

data/test.parquet

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+size 837607

data/train.parquet

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+oid sha256:f685bd6ef7a390b0419d7a5bdd84de696a9870e100a0f23b0a31bbd90f00b312
+size 2309320

data/val.parquet

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+oid sha256:c4818a12b5fc91f7870ad0dbe6b128e30476426436bd8abe76b7706d03d902d7
+size 837133

metadata.json

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+{
+  "READY": true,
+  "name": "MTBLS136",
+  "task": "binary",
+  "num_classes": 2,
+  "cat_idx": [],
+  "num_samples": 668,
+  "num_features": 915,
+  "imbalance_ratio": 1.0181,
+  "description": "A serum LC-MS dataset consisting of 949 named metabolites. We compare estrogen plus progestin (E+P; Class=1) vs. estrogen-only (E; Class=0).",
+  "source": "CIMBC/MetabComparisonBinaryML",
+  "label": "Metabolomics",
+  "sub_labels": [
+    "mass spectrometry",
+    "LC-MS"
+  ],
+  "force_scale": true
+}