Complex harmonic regularization with differential evolution in a memetic framework for biomarker selection.

ABSTRACT: For studying cancer and genetic diseases, the issue of identifying high correlation genes from high-dimensional data is an important problem. It is a great challenge to select relevant biomarkers from gene expression data that contains some important correlation structures, and some of the genes can be divided into different groups with a common biological function, chromosomal location or regulation. In this paper, we propose a penalized accelerated failure time model CHR-DE using a non-convex regularization (local search) with differential evolution (global search) in a wrapper-embedded memetic framework. The complex harmonic regularization (CHR) can approximate to the combination [Formula: see text] and ℓq (1 ≤ q < 2) for selecting biomarkers in group. And differential evolution (DE) is

SUBMITTER: Wang S 

PROVIDER: S-EPMC6375558 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature