4 citations · 7 across the 7 of their papers we have counts for
12 papers
Bayesian functional graphical models
Lin Zhang, Veera Baladandayuthapani, Quinton Neville +2
We develop a Bayesian graphical modeling framework for functional data for correlated multivariate random variables observed over a continuous domain. Our method leads to graphical…
Tumor Radiogenomics with Bayesian Layered Variable Selection
Shariq Mohammed, Sebastian Kurtek, Karthik Bharath +2
We propose a statistical framework to integrate radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade g…
RADIOHEAD: Radiogenomic Analysis Incorporating Tumor Heterogeneity in Imaging Through Densities
Shariq Mohammed, Karthik Bharath, Sebastian Kurtek +2
Recent technological advancements have enabled detailed investigation of associations between the molecular architecture and tumor heterogeneity, through multi-source integration o…
Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer
Zeya Wang, Veera Baladandayuthapan, Ahmed O. Kaseb +4
Graphical models are commonly used to discover associations within gene or protein networks for complex diseases such as cancer. Most existing methods estimate a single graph for a…
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Yabo Niu, Nilabja Guha, Debkumar De +3
Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorpo…
Bayesian Structure Learning in Multi-layered Genomic Networks
Min Jin Ha, Francesco Stingo, Veerabhadran Baladandayuthapani
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 informatio…