title: Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation.
abstract: Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China ; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, China ; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Guo
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China ; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Ye
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Wenhao
authors.initials: W
authors.lastname: Hu
authors.affiliation: Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China ; Department of Physics and Informatics, Shantou University Medical College, Shantou, China.
authors.forename: Haizhu
authors.initials: H
authors.lastname: Tan
authors.affiliation: Department of Statistical Science, School of Mathematics & Computational Science, Sun Yat-Sen University, Guangzhou, China ; Southern China Research Center of Statistical Science, Sun Yat-Sen University, Guangzhou, China ; Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China ; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China.
authors.forename: Xueqin
authors.initials: X
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics*; D015964:Gene Expression Regulation, Bacterial; D053263:Gene Regulatory Networks; D017711:Nonlinear Dynamics; D012372:ROC Curve; D015203:Reproducibility of Results; D013014:SOS Response, Genetics / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics