Paper Info
Title
A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions.
Abstract
Detection of epistatic interactions, which are referred to as nonlinear interactive effects of single nucleotide polymorphisms (SNPs), is increasingly being recognized as an important route in capturing the underlying genetic causes of complex diseases. Its methodological and computational challenges have been well understood, and many methods also have been proposed from different perspectives. Among them ant colony optimization (ACO)-based methods are promising due to their controllable time complexities, heuristic positive feedback search, and high detection power. Nevertheless, there is no comprehensive overview of them so far. This paper, therefore, provides a systematic review of 25 ACO-based epistasis detection methods. First, the generic ACO algorithm, as well as how it is applied to detect epistatic interactions, is briefly described. Then, an in-depth review of ACO-based methods for detecting epistatic interactions is discussed from four aspects, including path selection strategies, pheromone updating rules, fitness functions, and two-stage designs. Finally, this paper analyzes the strengths and limitations of involved methods, provides guidelines for applying them, and gives several views on the future directions of epistasis detection methods.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2894676
IEEE ACCESS
Keywords
Field
DocType
Ant colony optimization (ACO),epistatic interactions,single nucleotide polymorphisms (SNPs),heuristic information,genome-wide association studies (GWAS)
Ant colony optimization algorithms,Epistasis,Computer science,Artificial intelligence,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Junliang Shang14214.78
Xuan Wang201.01
Xiaoyang Wu300.34
Yingxia Sun441.44
Qian Ding5855.31
Liu Jin-Xing64016.11
Honghai Zhang71656104.27