Personalized Integrated Network Estimation

• Activity Number: 518 - Modern Graphical Modeling of Complex Biomedical Systems
• Type: Invited
• Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
• Sponsor: ENAR
• Author(s): Veera Baladandayuthapani* and Min Jin Ha and Francesco Stingo and Yang Ni
• Companies: University of Michigan and UT MD Anderson Cancer Center and University of Florence and Texas A&M University
• Keywords: Bayesian; Graphical Models; Genomics; Cancer; Precision Medicine; Networks

Abstract

Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across patient populations. In the context of cancer, the functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We consider the problem of modeling conditional independence structures in heterogenous data using Bayesian graphical regression techniques that allows patient-specific network estimation and inferences. We propose a novel specification of a conditional (in)dependence function of patient-specific 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.