Title: Bayesian multi-layered Gaussian graphical models
Authors: Min Jin Ha - UT MD Anderson Cancer Center (United States) [presenting]
Abstract: Simultaneous modeling of data arising from multiple ordered layers provides insight into the holistic picture of the interactive system and the flow of information. Chain graphs have been used to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers that exhibit undirected and directed acyclic relations within and between the layers. We develop a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in probabilistic chain graphs. Our proposed model uses a Bayesian node-wise selection framework that coherently accounts for dependencies in the mlGGM. Using Bayesian variable selection strategies for each of the node-wise regressions allows for flexible modeling, sparsity and incorporation of edge-specific prior knowledge. Through simulated data generated from various scenarios, we demonstrate that our node-wise regression method outperforms other related multivariate regression-based methodologies. We apply mlGGM to identify integrative networks for key signaling pathways in kidney cancer and dynamic signaling networks using longitudinal proteomics data in breast cancer.