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Activity Number: 59 - Invited E-Poster Session I
Type: Invited
Date/Time: Sunday, August 8, 2021 : 5:45 PM to 6:30 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #317327
Title: Personalized Integrated Network Estimation
Author(s): Veerabhadran Baladandayuthapani*
Companies: University of Michigan
Keywords: Bayesian modeling; Network Estimation; Data Integration; Cancer ; Genomics; Sparse learning

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 or undirected 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.

Authors who are presenting talks have a * after their name.

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