GNNenrich: a novel method for pathway enrichment analysis based on graph neural network.

IF 5.4

Bioinformatics (Oxford, England) Pub Date : 2025-09-01 DOI:10.1093/bioinformatics/btaf478

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

{"title":"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","doi":"10.1093/bioinformatics/btaf478","DOIUrl":null,"url":null,"abstract":"Motivation: 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.Results: 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).Availability and implementation: Source code is available at https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo.","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448840/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btaf478","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Motivation: 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.

Results: 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).

Availability and implementation: Source code is available at https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo.

查看原文本刊更多论文

GNNenrich：一种基于图神经网络的路径富集分析新方法。

动机：图神经网络（GNN）模型已经出现在许多领域，特别是由基因或蛋白质及其相互作用构成的生物网络。大多数富集研究方法采用过代表性分析和基因/蛋白质集评分，根据现有的途径之间的重叠。这些方法忽略了来自基因/蛋白质组之间相互作用的知识。在这里，我们介绍了一种新的基于gnn的富集分析方法，称为GNNenrich。GNNenrich通过整合蛋白质序列特性和相互作用网络的多层次嵌入，建立了支持生物学解释的功能关系。结果：GNNenrich已经过测试，并与过度代表性分析技术（如Profiler）和基于图的方法（enrichment net）进行了比较。它展示了复制其他方法提供的结果的能力，并为解释提供了新的视角，返回由蛋白质-蛋白质相互作用（PPIs）支持的相关结果。可用性和实现：源代码可从https://gitlab.com/cnrgh/gnn-enrich/gnn-enrich-article-demo获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Bioinformatics (Oxford, England)

自引率

0.00%

发文量