Evaluation of hierarchical models for integrative genomic analyses.

ABSTRACT:

Motivation

Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of data.

Results

Flexible modeling approaches, based on penalized likelihood methods and expectation-maximization (EM) algorithms, are studied and tested under various biological relationship scenarios between the different molecular features and their effects on a clinical outcome. The models are applied to

SUBMITTER: Denis M 

PROVIDER: S-EPMC5939889 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature