A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies

DOI10.1080/02664763.2018.1444741OpenAlexW2964017206MaRDI QIDQ5036536

Sourabh Bhattacharya, Durba Bhattacharya

Publication date: 23 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.7571

zbMATH Keywords

Dirichlet processparallel processingcase-control studymyocardial infarctiongene-gene interactiontransformation-based MCMC

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items (1)

Effects of gene-environment and gene-gene interactions in case-control studies: a novel Bayesian semiparametric approach

Uses Software

• Cecil
• SPA3G

Cites Work

• On Bayesian ``central clustering: application to landscape classification of Western Ghats
• Gene-centric gene-gene interaction: a model-based kernel machine method
• Gibbs sampling based Bayesian analysis of mixtures with unknown number of components
• Markov chains for exploring posterior distributions. (With discussion)
• Theory of statistics
• Fast and efficient Bayesian semi-parametric curve-fitting and clustering in massive data
• Effects of gene-environment and gene-gene interactions in case-control studies: a novel Bayesian semiparametric approach
• A Novel Bayesian Semiparametric Algorithm for Inferring Population Structure and Adjusting for Case‐Control Association Tests

This page was built for publication: A Bayesian semiparametric approach to learning about gene–gene interactions in case-control studies