title: Joint Bayesian variable and graph selection for regression models with network-structured predictors.
abstract: In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications because it allows the identification of pathways of functionally related genes or proteins that impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival.
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, CA, 94305, U.S.A.
authors.forename: Christine B
authors.initials: CB
authors.lastname: Peterson
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, U.S.A.
authors.forename: Francesco C
authors.initials: FC
authors.lastname: Stingo
authors.affiliation: Department of Statistics, Rice University, Houston, TX, 77005, U.S.A.
authors.forename: Marina
authors.identifier: http://orcid.org/0000-0002-7360-5321
authors.initials: M
authors.lastname: Vannucci
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics; D001932:Brain Neoplasms / Q000378:metabolism; D003196:Computer Graphics; D003198:Computer Simulation; D005909:Glioblastoma / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D060066:Protein Interaction Maps; D012044:Regression Analysis
keywords: Bayesian variable selection; Gaussian graphical model; linear model; protein network