DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding

推论 基因调控网络 计算机科学 水准点(测量) 嵌入 计算生物学 代表(政治) 图形 基因表达调控 机器学习 人工智能 基因 数据挖掘 基因表达 生物 理论计算机科学 遗传学 大地测量学 政治 政治学 法学 地理
作者
Zhen Gao,Yansen Su,Junfeng Xia,Ruifen Cao,Yun Ding,Chun-Hou Zheng,Pi-Jing Wei
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:25 (3) 被引量:7
标识
DOI:10.1093/bib/bbae143
摘要

Abstract The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

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