4 citations · 7 across the 7 of their papers we have counts for
9 papers · 1 filter
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 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…
Regression Analyses of Distributions using Quantile Functional Regression
Hojin Yang, Veerabhadran Baladandayuthapani, Arvind U. K. Rao +1
Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image…