title: An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.
abstract: SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
authors.forename: Junliang
authors.initials: J
authors.lastname: Shang
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Sun
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
authors.forename: Shengjun
authors.initials: S
authors.lastname: Li
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China ; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230039, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.affiliation: School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
authors.forename: Junying
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D004843:Epistasis, Genetic; D006801:Humans; D015810:Linkage Disequilibrium / Q000235:genetics; D008268:Macular Degeneration / Q000235:genetics; D019683:Penetrance; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*