A graph neural network that combines scRNA-seq and protein–protein interaction data

IF 32.1 1区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Nature Methods Pub Date : 2025-03-18 DOI:10.1038/s41592-025-02628-z

引用次数: 0

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.

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结合scRNA-seq和蛋白-蛋白相互作用数据的图神经网络。

我们引入了一种称为scNET的双视图图神经网络（GNN）框架，该框架将scRNA-seq数据与蛋白质-蛋白质相互作用网络集成在一起。这种方法增强了基因功能、途径和基因关系的表征，改善了细胞聚类和不同条件下差异激活生物途径的识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Methods 生物-生化研究方法

CiteScore

58.70

自引率

1.70%

发文量

326

审稿时长

1 months

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.