Ant colony optimization with an automatic adjustment mechanism for detecting epistatic interactions. (December 2018)

• Record Type: Journal Article
• Authors: Guan, Boxin; Zhao, Yuhai; Sun, Wenjuan
• Is Part Of: Computational biology and chemistry. Volume 77(2018)
• Page Start: 354
• Page End: 362
• Publication Date: 2018-12
• Subjects: Epistatic interactions -- Ant colony optimization -- Single nucleotide polymorphisms -- Automatic adjustment mechanism; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.11.001
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A novel ant colony optimization with an automatic adjustment mechanism (AA-ACO) is proposed. Automatic adjustment mechanism automatically can adjust the behaviour of artificial ants according to the real-time feedback information. AA-ACO can improve the diversity of solutions and reduce the probability of falling into local optima, so that the epistasis detection power of the method is enhanced in the large-scale data set. Detail experiment design has been given for evaluating the proposed method based on a set of simulated data sets and a real genome-wide data. Abstract: Single Nucleotide polymorphisms (SNPs) are usually used as biomarkers for research and analysis of genome-wide association study (GWAS). Moreover, the epistatic interaction of SNPs is an important factor in determining the susceptibility of individuals to complex diseases. Nowadays, the detection of epistatic interactions not only attracts attention of many researchers but also brings new challenges. It is of great significance to mine epistatic interactions from large-scale data for the combinatorial explosion problem of loci. Hence, it is necessary to improve an efficient algorithm for solving the problem. In this article, a novel ant colony optimization based on automatic adjustment mechanism (AA-ACO) is proposed. The mechanism automatically adjusts the behaviour of artificial ants according to the real-time feedback information so that the algorithm can run at its best. This study also compares AA-ACO with ACO, AntEpiSeeker, AntMiner, MACOED and epiACO in a set of simulated data sets and a real genome-wide data. As shown by the experimental results, the proposed algorithm is superior to the other algorithms.