Nevin Manimala Statistics

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

Bioinformatics. 2023 Nov 21:btad703. doi: 10.1093/bioinformatics/btad703. Online ahead of print.


SUMMARY: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA).

AVAILABILITY: The source code is available at, and the data at Zenodo (DOI: 10.5281/zenodo.8305122).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37988152 | DOI:10.1093/bioinformatics/btad703

By Nevin Manimala

Portfolio Website for Nevin Manimala