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geometric-intelligence/ogbench

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… See the full description on the dataset page: https://huggingface.co/datasets/geometric-intelligence/ogbench.

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1---2license: cc-by-4.03tags:4- biology5- genomics6- proteomics7- graph-neural-networks8- benchmarking9- omics10pretty_name: OgBench — Omics Graph Benchmark11task_categories:12- tabular-classification13size_categories:14- n<1K15---16 17# OgBench: Benchmarking Graph Neural Networks on Omics Data18 19OgBench is the first benchmark suite for graph-level prediction in the 20**n ≪ p regime** characteristic of omics data, where the number of 21patient samples n is much smaller than the number of nodes (genes or 22proteins) p per graph.23 24## Datasets25 26This repository contains four preprocessed omics graph classification 27datasets:28 29| Dataset | Modality | n | p | Task |30|---|---|---|---|---|31| HERITAGE | Proteomics | 654 | 4,977 | Exercise responder (binary) |32| Parkinson's | Transcriptomics | 535 | 21,755 | Cognitive status (binary) |33| AddNeuroMed | Transcriptomics | 711 | 17,198 | Clinical diagnosis (3-class) |34| BRCA | Epigenomics | 640 | 19,049 | Cancer subtype (4-class) |35 36## Source Data37 38- **HERITAGE**: Robbins et al. (2021), *Nature Metabolism*. Available 39  via MoTrPAC Data Hub (motrpac-data.org) under CC-BY 4.0.40- **Parkinson's**: Shamir et al. (2017), *Neurology*. Available via 41  NCBI GEO (GSE99039) under GEO public data access policy.42- **AddNeuroMed**: Lovestone et al. (2009). Available via NCBI GEO 43  (GSE63063) under GEO public data access policy.44- **BRCA**: Yang et al. (2025), MLOmics, *Scientific Data*. Available 45  on Figshare/Hugging Face under CC-BY 4.0.46 47## Preprocessing48 49All datasets are preprocessed with a consistent pipeline including 50probe-to-gene aggregation, normalization, and covariate adjustment. 51Full preprocessing details are provided in Appendix B of the 52accompanying paper. Graphs are split 70/15/15 (train/val/test) with 53a fixed random seed.