Sparse reduced-rank regression for integrating omics data.

ABSTRACT:

Background

The problem of assessing associations between multiple omics data including genomics and metabolomics data to identify biomarkers potentially predictive of complex diseases has garnered considerable research interest nowadays. A popular epidemiology approach is to consider an association of each of the predictors with each of the response using a univariate linear regression model, and to select predictors that meet a priori specified significance level. Although this approach is simple and intuitive, it tends to require larger sample size which is costly. It also assumes variables for each data type are independent, and thus ignores correlations that exist between variables both within each data type and across the data types.

Results

We consider a multivariate l

SUBMITTER: Hilafu H 

PROVIDER: S-EPMC7333421 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature