Adaptive modelling of gene regulatory network using Bayesian information criterion‐guided sparse regression approach. Issue 6 (1st December 2016)

• Record Type: Journal Article
• Authors: Shi, Ming; Shen, Weiming; Wang, Hong‐Qiang; Chong, Yanwen
• Is Part Of: IET systems biology. Volume 10:Issue 6(2016)
• Page Start: 252
• Page End: 259
• Publication Date: 2016-12-01
• Subjects: genetics -- Bayes methods -- genomics -- regression analysis -- inference mechanisms -- bioinformatics; adaptive modelling -- gene regulatory network -- Bayesian information criterion‐guided sparse regression approach -- GRN -- microarray expression data -- systems biology -- GRN reconstruction -- optimisation -- l1 ‐norm regularisation; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2016.0005
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)‐guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l 1 ‐norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real‐world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.