Bayesian Structure Learning in Multi-layered Genomic Networks.

ABSTRACT: Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structure

SUBMITTER: Ha MJ 

PROVIDER: S-EPMC8259335 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature