Joint Bayesian variable and graph selection for regression models with network‐structured predictors. (29th October 2015)

• Record Type: Journal Article
• Authors: Peterson, Christine B.; Stingo, Francesco C.; Vannucci, Marina
• Other Names: Heinze Georg guestEditor.; Michiels Stefan guestEditor.; Posch Martin guestEditor.
• Is Part Of: Statistics in medicine. Volume 35:Number 7(2016)
• Page Start: 1017
• Page End: 1031
• Publication Date: 2015-10-29
• Subjects: Bayesian variable selection -- Gaussian graphical model -- protein network -- linear model; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6792
• Languages: English
• ISSNs: 0277-6715

Abstract

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. Copyright © 2015 John Wiley & Sons, Ltd.