64 citations · 120 across the 29 of their papers we have counts for
29 papers
Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)
Neel Desai, Veerabhadran Baladandayuthapani, Russell T. Shinohara +1
We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivari…
Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks
Tsung-Hung Yao, Yang Ni, Anindya Bhadra +2
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of the two canonical…
Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data
Rupam Bhattacharyya, Nicholas Henderson, Veerabhadran Baladandayuthapani
Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understan…
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
Ksheera Sagar, Yang Ni, Veerabhadran Baladandayuthapani +1
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend…
Nonparametric Bayes Differential Analysis of Multigroup DNA Methylation Data
Chiyu Gu, Veerabhadran Baladandayuthapani, Subharup Guha
DNA methylation datasets in cancer studies are comprised of measurements on a large number of genomic locations called cytosine-phosphate-guanine (CpG) sites with complex correlati…
Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics
Moumita Chakraborty, Veerabhadran Baladandayuthapani, Anindya Bhadra +1
Integrative analysis of multi-level pharmacogenomic data for modeling dependencies across various biological domains is crucial for developing genomic-testing based treatments. Cha…
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…
Integrative Bayesian models using Post-selective Inference: a case study in Radiogenomics
Snigdha Panigrahi, Shariq Mohammed, Arvind Rao +1
Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clini…
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…
NExUS: Bayesian simultaneous network estimation across unequal sample sizes
Priyam Das, Christine Peterson, Kim-Anh Do +2
Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when esti…
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…
Bayesian Semiparametric Functional Mixed Models for Serially Correlated Functional Data, with Application to Glaucoma Data
Wonyul Lee, Michelle F. Miranda, Phlip Rausch +4
Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devi…
Quantile Functional Regression using Quantlets
Hojin Yang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris
In this paper, we develop a quantile functional regression modeling framework that models the distribution of a set of common repeated observations from a subject through the quant…
Nonparametric Bayes Differential Analysis for Dependent Multigroup Data with Application to DNA Methylation Analyses in Cancer
Chiyu Gu, Veerabhadran Baladandayuthapani, Subharup Guha
Modern cancer genomics datasets involve widely varying sizes and scales, measurement variables, and correlation structures. A fundamental analytical goal in these high-throughput s…
Radiologic Image-based Statistical Shape Analysis of Brain Tumors
Karthik Bharath, Sebastian Kurtek, Arvind Rao +1
We propose a curve-based Riemannian-geometric approach for general shape-based statistical analyses of tumors obtained from radiologic images. A key component of the framework is a…
A Nonparametric Bayesian Technique for High-Dimensional Regression
Subharup Guha, Veerabhadran Baladandayuthapani
This paper proposes a nonparametric Bayesian framework called VariScan for simultaneous clustering, variable selection, and prediction in high-throughput regression settings. Poiss…
Inferring network structure in non-normal and mixed discrete-continuous genomic data
Anindya Bhadra, Arvind Rao, Veerabhadran Baladandayuthapani
Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are poten…
Bayesian Nonparametric Graph Clustering
Sayantan Banerjee, Rehan Akbani, Veerabhadran Baladandayuthapani
We present clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables. As opposed to standard approaches that assume k…
Bayesian Variable Selection with Structure Learning: Applications in Integrative Genomics
Suprateek Kundu, Minsuk Shin, Yichen Cheng +3
Significant advances in biotechnology have allowed for simultaneous measurement of molecular data points across multiple genomic and transcriptomic levels from a single tumor/cance…
Nonparametric Variable Selection, Clustering and Prediction for High-Dimensional Regression
Subharup Guha, Veerabhadran Baladandayuthapani
The development of parsimonious models for reliable inference and prediction of responses in high-dimensional regression settings is often challenging due to relatively small sampl…