---
license: cc-by-4.0
tags:
- biology
- genomics
- proteomics
- graph-neural-networks
- benchmarking
- omics
pretty_name: OgBench — Omics Graph Benchmark
task_categories:
- tabular-classification
size_categories:
- n<1K
---

# 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.