An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.
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-...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/5/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109274722&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109274722 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/5/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109274722 109274722 109274722 10.1155/2015/524821 109274722 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions. aug: au: Shang, Junliang Sun, Yan Li, Shengjun Liu, Jin-Xing Zheng, Chun-Hou Zhang, Junying affil: School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China sug: subj: Computer Simulation Models, Statistical Particle Swarm Optimization Polymorphism, Single Nucleotide Disease Risk Factors Funding Source Disease Familial and Genetic ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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