Datasets:
license: cc-by-4.0
tags:
- biology
- genomics
- proteomics
- graph-neural-networks
- benchmarking
- omics
pretty_name: OmicsGBench — Omics Graph Benchmark
task_categories:
- tabular-classification
size_categories:
- n<1K
OmicsGBench: Benchmarking Graph Neural Networks on Omics Data
OmicsGBench is the first benchmark suite for graph-level prediction in the n ≪ p regime characteristic of omics data, where the number of patient samples n is much smaller than the number of nodes (genes or proteins) p per graph.
Datasets
This repository contains six preprocessed omics graph classification datasets, grouped by modality:
| Dataset | Modality | n | p | Task |
|---|---|---|---|---|
| HERITAGE | Proteomics | 654 | 4,976 | Exercise responder (binary) |
| Tuberculosis | Proteomics | 561 | 3,814 | Culture negative vs positive |
| Parkinson's | Transcriptomics | 535 | 21,755 | Cognitive status (binary) |
| AddNeuroMed | Transcriptomics | 711 | 17,197 | Clinical diagnosis (3-class) |
| Smoking | Epigenomics | 464 | 139,125 | Never- vs ever-smoker |
| BRCA | Epigenomics | 640 | 19,049 | Cancer subtype (4-class) |
Tuberculosis is a full-proteome antibody microarray (immunoproteomics), where each feature is one M. tuberculosis protein. Smoking stores 139,125 TSS1500/TSS200 methylation probes; training keeps one probe per gene, about 20,763 genes.
Source Data
- HERITAGE: Robbins et al. (2021), Nature Metabolism. Available via MoTrPAC Data Hub (motrpac-data.org) under CC-BY 4.0.
- Tuberculosis: Kunnath-Velayudhan et al. (2010), PNAS. Available via NCBI GEO (GSE19433) under GEO public data access policy.
- Parkinson's: Shamir et al. (2017), Neurology. Available via NCBI GEO (GSE99039) under GEO public data access policy.
- AddNeuroMed: Lovestone et al. (2009). Available via NCBI GEO (GSE63063) under GEO public data access policy.
- Smoking: Tsaprouni et al. (2014), Epigenetics. Available via NCBI GEO (GSE50660) under GEO public data access policy.
- BRCA: Yang et al. (2025), MLOmics, Scientific Data. Available on Figshare/Hugging Face under CC-BY 4.0.
Preprocessing
Datasets share one ingest pipeline: probe-to-gene aggregation where the source is probe-level, and the normalization described in Appendix B of the accompanying paper. Matrices on the Hub are uncorrected. HERITAGE covariate adjustment, AddNeuroMed ComBat, and smoking promoter-probe selection plus median-centering are fit on the training split only.
The default evaluation is stratified 5-fold. A 70/15/15 train/val/test split is available as split_type=fixed. Parkinson's and Tuberculosis keep each batch inside one fold: hybridization date for Parkinson's, country of serum collection for Tuberculosis.