title: Inference of gene regulatory networks from genetic perturbations with linear regression model.
abstract: It is an effective strategy to use both genetic perturbation data and gene expression data to infer regulatory networks that aims to improve the detection accuracy of the regulatory relationships among genes. Based on both types of data, the genetic regulatory networks can be accurately modeled by Structural Equation Modeling (SEM). In this paper, a linear regression (LR) model is formulated based on the SEM, and a novel iterative scheme using Bayesian inference is proposed to estimate the parameters of the LR model (LRBI). Comparative evaluations of LRBI with other two algorithms, the Adaptive Lasso (AL-Based) and the Sparsity-aware Maximum Likelihood (SML), are also presented. Simulations show that LRBI has significantly better performance than AL-Based, and overperforms SML in terms of power of detection. Applying the LRBI algorithm to experimental data, we inferred the interactions in a network of 35 yeast genes. An open-source program of the LRBI algorithm is freely available upon request.
authors.affiliation: School of Electronic Engineering, Huaihai Institute of Technology, Lianyungang, Jiangsu, China ; School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Zijian
authors.initials: Z
authors.lastname: Dong
authors.affiliation: School of Information Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Tiecheng
authors.initials: T
authors.lastname: Song
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hang Kong, China.
authors.forename: Chuang
authors.initials: C
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D016014:Linear Models; D008957:Models, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*
chemical_list: D029701:Saccharomyces cerevisiae Proteins