| --- |
| 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} |
| } |
| ``` |
|
|
