Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers.

ABSTRACT: In this paper, we propose a novel sparse regression method for Brain-Wide and Genome-Wide association study. Specifically, we impose a low-rank constraint on the weight coefficient matrix and then decompose it into two low-rank matrices, which find relationships in genetic features and in brain imaging features, respectively. We also introduce a sparse acyclic digraph with sparsity-inducing penalty to take further into account the correlations among the genetic variables, by which it can be possible to identify the representative SNPs that are highly associated with the brain imaging features. We optimize our objective function by jointly tackling low-rank regression and variable selection in a framework. In our method, the low-rank constraint allows us to conduct variable selection with t

SUBMITTER: Zhu X 

PROVIDER: S-EPMC5929142 | biostudies-literature | 2017 Oct-Dec

REPOSITORIES: biostudies-literature