TabBioMed camera-ready release

README.md

@@ -0,0 +1,97 @@
+---
+license: cc-by-4.0
+pretty_name: MultiOmics_GS-LGG
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- biomedical
+- benchmark
+- TabBioMed
+- systems-biology
+- 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
+---
+
+# MultiOmics_GS-LGG
+
+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.
+
+TCGA lower-grade glioma three-class classification, with 247 distinct patients. CNV (5,000), mRNA (3,435) and miRNA (102) features and targets are retained from MLOmics. DNA methylation is reconstructed from TCGA/Xena HumanMethylation450 by verified CpG identifiers and primary-tumour patient barcodes: 4,705 of the 5,000 original probes are assayed; the 295 unassayed probes are excluded. Missing beta values remain missing for fold-fitted imputation. Existing five outer folds and three-fold inner CV are retained. Patient identifiers are metadata, never features.
+
+| Domain | Task | Samples | Features | Classes |
+|---|---|---|---|---|
+| Systems Biology | classification | 247 | 13,242 | 3 |
+
+**Splits.** 5 stored outer cross-validation folds (`data/fold_<i>/`), each with train/val/test files (fold 0: 172/25/50 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).
+- `feature_names.json`: original feature names, in column order.
+- `sample_metadata.csv`: per-row provenance (identifiers, source annotations); not features.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("TabBioMed/MultiOmics_GS-LGG")
+```
+
+A Croissant (1.1) description with Responsible-AI fields is distributed with the
+TabBioMed release.
+
+## Source and license
+
+Source: MLOmics GS-LGG with TCGA/Xena HumanMethylation450
+
+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}
+}
+```

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metadata.json

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+{
+  "READY": true,
+  "name": "MultiOmics_GS-LGG",
+  "task": "classification",
+  "num_classes": 3,
+  "cat_idx": [],
+  "num_samples": 247,
+  "num_features": 13242,
+  "imbalance_ratio": 2.7174,
+  "description": "TCGA lower-grade glioma three-class classification, with 247 distinct patients. CNV (5,000), mRNA (3,435) and miRNA (102) features and targets are retained from MLOmics. DNA methylation is reconstructed from TCGA/Xena HumanMethylation450 by verified CpG identifiers and primary-tumour patient barcodes: 4,705 of the 5,000 original probes are assayed; the 295 unassayed probes are excluded. Missing beta values remain missing for fold-fitted imputation. Existing five outer folds and three-fold inner CV are retained. Patient identifiers are metadata, never features.",
+  "source": "MLOmics GS-LGG with TCGA/Xena HumanMethylation450",
+  "split_strategy": "cv",
+  "label": "systems biology",
+  "source_url": "https://tcga.xenahubs.net/download/TCGA.LGG.sampleMap/HumanMethylation450.json",
+  "sample_metadata_file": "sample_metadata.csv",
+  "methylation_protocol": "lgg_xena_methylation450_v1",
+  "source_manifest_sha256": "ccc526005cc44e2acb0f4a69c1b04c0505f0cb5d68a787b48fa31237f7e84007",
+  "omics_feature_counts": {
+    "CNV": 5000,
+    "Methy": 4705,
+    "miRNA": 102,
+    "mRNA": 3435
+  }
+}

sample_metadata.csv

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