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.