ArticleBioinformatics (Oxford, England)2025
GNNenrich: a novel method for pathway enrichment analysis based on graph neural network.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis.NPJ digital medicine · 2026Article
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
motivationGraph 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.
resultsGNNenrich 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). AVAILABILITY AND IMPLEMENTATION: Source code is available at https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo.
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