Identifying disease-associated biomarker network features through conditional graphical model.

ABSTRACT: Biomarkers are often organized into networks, in which the strengths of network connections vary across subjects depending on subject-specific covariates (eg, genetic variants). Variation of network connections, as subject-specific feature variables, has been found to predict disease clinical outcome. In this work, we develop a two-stage method to estimate biomarker networks that account for heterogeneity among subjects and evaluate network's association with disease clinical outcome. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain covariate-dependent networks with connection strengths varying across subjects while assuming homogeneous network structure. In the second stage, we evaluate clinical utility

SUBMITTER: Xie S 

PROVIDER: S-EPMC12933381 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature