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B1800
Title: Bayesian graphical regression Authors:  Francesco Stingo - University of Florence (Italy) [presenting]
Veerabhadran Baladandayuthapani - UT MD Anderson Cancer Center (United States)
Abstract: The purpose is to consider the problem of modeling conditional independence structures in heterogeneous data in the presence of additional subject-level covariates termed Graphical Regression is considered. We propose a novel specification of a conditional (in)dependence function of covariates which allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks.