| --- |
| 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. |
