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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.

SUBMITTER: Peterson CB

PROVIDER: S-EPMC4775388 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Joint Bayesian variable and graph selection for regression models with network-structured predictors.

Peterson Christine B CB Stingo Francesco C FC Vannucci Marina M

Statistics in medicine 20151029 7

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 p ...[more]

PMID: 26514925

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