A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis. - Benjamin A. Logsdon; Gabriel E. Hoffman; Jason G. Mezey (BMC Bioinform., 2010)

Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations. - Benjamin A. Logsdon; Jason G. Mezey (PLoS Comput. Biol., 2010)

Empirical Bayes conditional independence graphs for regulatory network recovery. - Rami Mahdi; Abishek S. Madduri; Guoqing Wang; Yael Strulovici-Barel; Jacqueline Salit; Neil R. Hackett; Ronald G. Crystal; Jason G. Mezey (Bioinform., 2012)

Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control. - Benjamin A. Logsdon; Gabriel E. Hoffman; Jason G. Mezey (BMC Bioinform., 2012)

Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion. - Rami Mahdi; Jason G. Mezey (J. Mach. Learn. Res., 2013)

PUMA: A Unified Framework for Penalized Multiple Regression Analysis of GWAS Data. - Gabriel E. Hoffman; Benjamin A. Logsdon; Jason G. Mezey (PLoS Comput. Biol., 2013)

HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors. - Chuan Gao; Nicole L. Tignor; Jacqueline Salit; Yael Strulovici-Barel; Neil R. Hackett; Ronald G. Crystal; Jason G. Mezey (Bioinform., 2014)

lrgpr: interactive linear mixed model analysis of genome-wide association studies with composite hypothesis testing and regression diagnostics in R. - Gabriel E. Hoffman; Jason G. Mezey; Eric E. Schadt (Bioinform., 2014)

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci. - Jin Hyun Ju; Sushila A. Shenoy; Ronald G. Crystal; Jason G. Mezey (PLoS Comput. Biol., 2017)

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes. - Afrah Shafquat; Ronald G. Crystal; Jason G. Mezey (BMC Bioinform., 2020)

Simulants: Synthetic Clinical Trial Data via Subject-Level Privacy-Preserving Synthesis. - Mandis Beigi; Afrah Shafquat; Jason G. Mezey; Jacob Aptekar (AMIA, 2022)

SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning. - Trisha Das; Zifeng Wang 008; Afrah Shafquat; Mandis Beigi; Jason G. Mezey; Jimeng Sun 001 (CoRR, 2024)