A0346
Title: Bayesian edge regression: Characterizing observation-specific heterogeneity in estimating undirected graphical models
Authors: Zeya Wang - University of Kentucky (United States) [presenting]
Abstract: Bayesian edge regression is a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. By doing so, this model allows for the accounting of observation-specific heterogeneity in estimating networks. Two case studies are presented using the proposed model: one is a set of simulation studies focused on comparing tumor and normal networks while adjusting for tumor purity; the other is an application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), in which the variation in blood protein networks with disease severity is ascertained.
