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Joint Bayesian variable and graph selection for regression models with network‐structured predictors
Author(s) -
Peterson Christine B.,
Stingo Francesco C.,
Vannucci Marina
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6792
Subject(s) - graphical model , computer science , feature selection , inference , bayesian network , conditional dependence , machine learning , model selection , artificial intelligence , data mining , identification (biology) , graph , directed acyclic graph , regression , bayesian probability , regression analysis , statistics , algorithm , mathematics , theoretical computer science , botany , biology
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.