Crow Search Algorithm Based on Information Interaction for Epistasis Detection.

In the genome-wide association study, the interactions of single nucleotide polymorphisms (SNPs) play an important role in revealing the genetic mechanism of complex diseases, and such interaction is called epistasis or epistatic interactions. In recent years, swarm intelligence methods have been widely used to detect epistatic interactions because they can effectively deal with global optimization problems. In this study, we propose a crow search algorithm based on information interaction (FICSA) to detect epistatic interactions. FICSA combines particle swarm optimization (PSO) and crow search algorithm (CSA) to balance the exploration and exploitation in the search process, which can effectively improve the ability of the algorithm to detect epistatic interactions. In addition, opposition-based learning strategy and adaptive parameters are used to further improve the performance of the algorithm. We compare FICSA with seven other epistasis detection algorithms using both simulated datasets and a real-life age-related macular degeneration (AMD) dataset. The results on simulated datasets show that FICSA has better detection power, while the results on the real dataset demonstrate the effectiveness of the proposed algorithm.