GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 108 - 19
• Main Authors: Wang, Kai, Li, Yulong, Liu, Fei, Luan, Xiaoli, Wang, Xinglong, Zhou, Jingwen
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.04.2025; BioMed Central; BMC
• Subjects: Ablation; Algorithms; Anopheles; Computational Biology - methods; Data mining; Datasets; Deep Learning; Drug development; Gene expression; Gene expression data; Gene regulatory network; Gene Regulatory Networks; Gene sequencing; Genes; Genetic regulation; Genetic research; Graph representation learning; Graph representations; Graphical representations; Heterogeneity; Humans; Implicit links; Links; Machine Learning; Methods; Neural networks; Noise measurement; Performance evaluation; Predictions; Ribonucleic acid; RNA; RNA sequencing; RNA-Seq - methods; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-Cell Gene Expression Analysis; Stem cells; Transcription factors; China; Graph representation learning; Implicit links; Gene regulatory network; Gene expression data

A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug design, and metabolic systems, and the rapid development of sin...