title: Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach.
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 thel1-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.
authors.affiliation: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China.
authors.forename: Ming
authors.initials: M
authors.lastname: Shi
authors.affiliation: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China.
authors.forename: Weiming
authors.initials: W
authors.lastname: Shen
authors.affiliation: Machine Intelligence and Computational Biology Lab, Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, Hefei 230031, People's Republic of China.
authors.forename: Hong-Qiang
authors.initials: HQ
authors.lastname: Wang
authors.affiliation: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, People's Republic of China. ywchong@whu.edu.cn.
authors.forename: Yanwen
authors.initials: Y
authors.lastname: Chong
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D012372:ROC Curve; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D049490:Systems Biology