Detecting disease-associated genomic outcomes using constrained mixture of Bayesian hierarchical models for paired data.

ABSTRACT: Detecting disease-associated genomic outcomes is one of the key steps in precision medicine research. Cutting-edge high-throughput technologies enable researchers to unbiasedly test if genomic outcomes are associated with disease of interest. However, these technologies also include the challenges associated with the analysis of genome-wide data. Two big challenges are (1) how to reduce the effects of technical noise; and (2) how to handle the curse of dimensionality (i.e., number of variables are way larger than the number of samples). To tackle these challenges, we propose a constrained mixture of Bayesian hierarchical models (MBHM) for detecting disease-associated genomic outcomes for data obtained from paired/matched designs. Paired/matched designs can effectively reduce effects of con

SUBMITTER: Li Y 

PROVIDER: S-EPMC5373614 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature