README.md

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

# OgBench: Benchmarking Graph Neural Networks on Omics Data

[**Paper**](https://huggingface.co/papers/2605.15511) | [**GitHub**](https://github.com/geometric-intelligence/ogbench)

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) |

## Usage

After installing the [OgBench framework](https://github.com/geometric-intelligence/ogbench), you can download and process the datasets using the provided script:

```bash
# Download a specific dataset (e.g., motrpac/HERITAGE)
python scripts/download_datasets.py motrpac

# Download all datasets
python scripts/download_datasets.py all
```

To train a model (e.g., GATv2) on one of the datasets:

```bash
python ogbench/run.py dataset=motrpac model=gatv2
```

## 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.