{"title":"结合scRNA-seq和蛋白-蛋白相互作用数据的图神经网络。","authors":"","doi":"10.1038/s41592-025-02628-z","DOIUrl":null,"url":null,"abstract":"We introduce a dual-view graph neural network (GNN) framework called scNET that integrates scRNA-seq data with protein–protein interaction networks. This approach enhances the characterization of gene functions, pathways and gene–gene relationships and improves cell clustering and the identification of differentially activated biological pathways across conditions.","PeriodicalId":18981,"journal":{"name":"Nature Methods","volume":"22 4","pages":"660-661"},"PeriodicalIF":32.1000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A graph neural network that combines scRNA-seq and protein–protein interaction data\",\"authors\":\"\",\"doi\":\"10.1038/s41592-025-02628-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a dual-view graph neural network (GNN) framework called scNET that integrates scRNA-seq data with protein–protein interaction networks. This approach enhances the characterization of gene functions, pathways and gene–gene relationships and improves cell clustering and the identification of differentially activated biological pathways across conditions.\",\"PeriodicalId\":18981,\"journal\":{\"name\":\"Nature Methods\",\"volume\":\"22 4\",\"pages\":\"660-661\"},\"PeriodicalIF\":32.1000,\"publicationDate\":\"2025-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Methods\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41592-025-02628-z\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Methods","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41592-025-02628-z","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
A graph neural network that combines scRNA-seq and protein–protein interaction data
We introduce a dual-view graph neural network (GNN) framework called scNET that integrates scRNA-seq data with protein–protein interaction networks. This approach enhances the characterization of gene functions, pathways and gene–gene relationships and improves cell clustering and the identification of differentially activated biological pathways across conditions.
期刊介绍：
Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
