title: Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.
abstract: Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
authors.affiliation: Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Yue
authors.identifier: 0000-0002-5597-3884
authors.initials: Y
authors.lastname: Fan
authors.affiliation: Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Xiao
authors.identifier: 0000-0003-4206-9199
authors.initials: X
authors.lastname: Wang
authors.affiliation: Systems Engineering Institute, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Qinke
authors.identifier: 0000-0002-2697-1038
authors.initials: Q
authors.lastname: Peng
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D006367:HeLa Cells; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D012044:Regression Analysis