Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.

ABSTRACT: In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian

SUBMITTER: Gu Y 

PROVIDER: S-EPMC9140669 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature