KEGNI: knowledge graph enhanced framework for gene regulatory network inference

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2025-09-22 DOI:10.1186/s13059-025-03780-7

Pengxiao Li, Lin Li, Jingminjie Nan, Jiahuan Chen, Jielin Sun, Yanan Cao

引用次数: 0

Abstract

Inference of cell type-specific gene regulatory networks (GRNs) is a fundamental step in investigating complex regulatory mechanisms. Here, we present KEGNI (Knowledge graph-Enhanced Gene regulatory Network Inference), a knowledge-guided framework that employs a graph autoencoder to capture gene regulatory relationships and incorporates a knowledge graph to infer GRNs based on scRNA-seq data. KEGNI shows superior performance compared to multiple methods using scRNA-seq data or paired scRNA-seq and scATAC-seq data. KEGNI can identify driver genes and elucidate the regulatory mechanisms underlying distinct cellular contexts. The modular design of KEGNI supports the integration of various knowledge graphs for context-specific tasks.

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KEGNI：基因调控网络推理的知识图谱增强框架

细胞类型特异性基因调控网络（GRNs）的推断是研究复杂调控机制的基本步骤。在这里，我们提出了KEGNI （Knowledge graph- enhanced Gene regulatory Network Inference，知识图增强基因调控网络推断），这是一个知识引导框架，它使用图自编码器来捕获基因调控关系，并结合知识图来推断基于scRNA-seq数据的grn。与使用scRNA-seq数据或配对scRNA-seq和scATAC-seq数据的多种方法相比，KEGNI显示出优越的性能。KEGNI可以识别驱动基因并阐明不同细胞背景下的调控机制。KEGNI的模块化设计支持为特定于上下文的任务集成各种知识图。

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

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

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