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OgBench: Benchmarking Graph Neural Networks on Omics Data
OgBench 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 four preprocessed omics graph classification datasets:
| Dataset | Modality | n | p | Task |
|---|---|---|---|---|
| HERITAGE | Proteomics | 654 | 4,977 | Exercise responder (binary) |
| Parkinson's | Transcriptomics | 535 | 21,755 | Cognitive status (binary) |
| AddNeuroMed | Transcriptomics | 711 | 17,198 | Clinical diagnosis (3-class) |
| BRCA | Epigenomics | 640 | 19,049 | Cancer subtype (4-class) |
Source Data
- HERITAGE: Robbins et al. (2021), Nature Metabolism. Available via MoTrPAC Data Hub (motrpac-data.org) under CC-BY 4.0.
- 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.
- BRCA: Yang et al. (2025), MLOmics, Scientific Data. Available on Figshare/Hugging Face under CC-BY 4.0.
Preprocessing
All datasets are preprocessed with a consistent pipeline including probe-to-gene aggregation, normalization, and covariate adjustment. Full preprocessing details are provided in Appendix B of the accompanying paper. Graphs are split 70/15/15 (train/val/test) with a fixed random seed.
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