Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease.

ABSTRACT: The problem of associating data from multiple sources and predicting an outcome simultaneously is an important one in modern biomedical research. It has potential to identify multidimensional array of variables predictive of a clinical outcome and to enhance our understanding of the pathobiology of complex diseases. Incorporating functional knowledge in association and prediction models can reveal pathways contributing to disease risk. We propose Bayesian hierarchical integrative analysis models that associate multiple omics data, predict a clinical outcome, allow for prior functional information, and can accommodate clinical covariates. The models, motivated by available data and the need for exploring other risk factors of atherosclerotic cardiovascular disease (ASCVD), are used for inte

SUBMITTER: Chekouo T 

PROVIDER: S-EPMC9960952 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature