A modeling framework for detecting and leveraging node-level information in Bayesian network inference.

ABSTRACT: Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precisio

SUBMITTER: Xi X 

PROVIDER: S-EPMC11823055 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature