GNNenrich: a novel method for pathway enrichment analysis based on Graph Neural Network.

Authors

Mallek Mziou-Sallami, Pierrick Roger, Arnaud Gloaguen, Claire Dandine-Roulland, Thierry Jiogho Ngaho, Solène Brohard, Kévin Muret, Florian Sandron, Eric Bonnet, Jean-Francois Deleuze, Edith Le Floch, Vincent Meyer

Published in

Bioinformatics (Oxford, England). Sep 08, 2025. Epub Sep 08, 2025.

Abstract

Graph Neural Network (GNN) models have emerged in many fields and notably for biological networks constituted by genes or proteins and their interactions. The majority of enrichment study methods apply over-representation analysis and gene/protein set scores according to the existing overlap between pathways. Such methods neglect knowledges coming from the interactions between the gene/protein sets.Here, we introduce a novel GNN-based enrichment analysis method called GNNenrich. GNNenrich, through multiple levels of embedding that integrate protein sequence properties and interactions network, establishes functional relationship to support biological interpretation.
GNNenrich have been tested and compared to Over-Representation Analysis technique (g: Profiler) and graph-based method (EnrichNet). It demonstrates the capacity to reproduce results provided by others approaches and offers new perspectives for interpretation, returning relevant results supported by Protein-Protein Interactions (PPIs).
Source code is available at https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo.

PMID:
40920362
Bibliographic data and abstract were imported from PubMed on 08 Sep 2025.

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