Integrative analysis of multiple diverse omics datasets by sparse group multitask regression.

ABSTRACT: A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a t

SUBMITTER: Lin D 

PROVIDER: S-EPMC4209817 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature