Enhanced Construction of Gene Regulatory Networks Using Hub Gene Information

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2017 Mar 25

PMID 28335719

Citations 54

Authors

Donghyeon Yu

Johan Lim

Xinlei Wang

Faming Liang

Guanghua Xiao

Affiliations

Soon will be listed here.

Abstract

Background: Gene regulatory networks reveal how genes work together to carry out their biological functions. Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for biomarker and drug discoveries. In gene networks, a gene that has many interactions with other genes is called a hub gene, which usually plays an essential role in gene regulation and biological processes. In this study, we developed a method for reconstructing gene networks using a partial correlation-based approach that incorporates prior information about hub genes. Through simulation studies and two real-data examples, we compare the performance in estimating the network structures between the existing methods and the proposed method.

Results: In simulation studies, we show that the proposed strategy reduces errors in estimating network structures compared to the existing methods. When applied to Escherichia coli, the regulation network constructed by our proposed ESPACE method is more consistent with current biological knowledge than the SPACE method. Furthermore, application of the proposed method in lung cancer has identified hub genes whose mRNA expression predicts cancer progress and patient response to treatment.

Conclusions: We have demonstrated that incorporating hub gene information in estimating network structures can improve the performance of the existing methods.

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