17 citations · 24 across the 16 of their papers we have counts for
16 papers
Supervised Low-Rank Structure Discovery for Developmental Epigenetic Aging in Ultra-High-Dimensional DNA Methylation Data
Priyam Das, Jiyeon Song, Lathika Mohanraj +3
Ultra-high-dimensional array-based CpG methylation studies require statistical frameworks that simultaneously provide supervised structure discovery, interpretability, scalable lat…
Enhancing Inference for Small Cohorts via Transfer Learning and Weighted Integration of Multiple Datasets
Subharup Guha, Mengqi Xu, Yi Li
Lung sepsis remains a significant concern in the Northeastern U.S., yet the national eICU Collaborative Database includes only a small number of patients from this region, highligh…
WMAP: An R Package for Causal Meta-Analysis by Integrating Multiple Observational Studies
Subharup Guha, Mengqi Xu, Kashish Priyam +2
Integrating multiple observational studies for meta-analysis has sparked much interest. The presented R package WMAP (Weighted Meta-Analysis with Pseudo-Populations) (Guha et al.,…
Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates
Subharup Guha, Yi Li
Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, P…
Causal Meta-Analysis by Integrating Multiple Observational Studies with Multivariate Outcomes
Subharup Guha, Yi Li
Integrating multiple observational studies to make unconfounded causal or descriptive comparisons of group potential outcomes in a large natural population is challenging. Moreover…
Unconfounded Meta-analytical Frameworks for Multivariate Outcomes in Multigroup Observational Studies using Concordant Weights
Subharup Guha, David C. Christiani, S. V. Subramanian +1
While meta-analyzing retrospective cancer patient cohorts, an investigation of differences in the expressions of target oncogenes across cancer subtypes is of substantial interest…
A New Integrative Method for Multigroup Comparisons of Censored Survival Outcomes in Multiple Observational Studies
Subharup Guha, David C. Christiani, Yi Li
In observational studies, covariate imbalance generates confounding, resulting in biased comparisons. Although propensity score-based weighting approaches facilitate unconfounded g…
A Bayesian Nonparametric Approach for Identifying Differentially Abundant Taxa in Multigroup Microbiome Data with Covariates
Archie Sachdeva, Somnath Datta, Subharup Guha
Scientific studies in the last two decades have established the central role of the microbiome in disease and health. Differential abundance analysis seeks to identify microbial ta…
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…
A Clustering Approach to Integrative Analysis of Multiomic Cancer Data
Dongyan Yan, Subharup Guha
Rapid technological advances have allowed for molecular profiling across multiple omics domains from a single sample for clinical decision making in many diseases, especially cance…
Probabilistic Detection and Estimation of Conic Sections from Noisy Data
Subharup Guha, Sujit K. Ghosh
Inferring unknown conic sections on the basis of noisy data is a challenging problem with applications in computer vision. A major limitation of the currently available methods for…
Predicting Phenotypes from Brain Connection Structure
Subharup Guha, Rex Jung, David Dunson
This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictiv…
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…
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…
hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data
Shiqi Cui, Subharup Guha, Marco A. R. Ferreira +1
We introduce hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov m…
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…