A genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPs

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dc.contributor.authorIlhan, Ilhan
dc.contributor.authorTezel, Gulay
dc.date.accessioned2020-03-26T18:40:59Z
dc.date.available2020-03-26T18:40:59Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSNPs (Single Nucleotide Polymorphisms) include millions of changes in human genome, and therefore, are promising tools for disease-gene association studies. However, this kind of studies is constrained by the high expense of genotyping millions of SNPs. For this reason, it is required to obtain a suitable subset of SNPs to accurately represent the rest of SNPs. For this purpose, many methods have been developed to select a convenient subset of tag SNPs, but all of them only provide low prediction accuracy. In the present study, a brand new method is developed and introduced as GA-SVM with parameter optimization. This method benefits from support vector machine (SVM) and genetic algorithm (GA) to predict SNPs and to select tag SNPs, respectively. Furthermore, it also uses particle swarm optimization (PSO) algorithm to optimize C and gamma parameters of support vector machine. It is experimentally tested on a wide range of datasets, and the obtained results demonstrate that this method can provide better prediction accuracy in identifying tag SNPs compared to other methods at present. (c) 2012 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects Coordinatorship, Konya, TurkeySelcuk Universityen_US
dc.description.sponsorshipThis study is supported by Selcuk University Scientific Research Projects Coordinatorship, Konya, Turkey. The authors would like to thank the editors and anonymous reviewers of this manuscript for their very helpful suggestions.en_US
dc.identifier.doi10.1016/j.jbi.2012.12.002en_US
dc.identifier.endpage340en_US
dc.identifier.issn1532-0464en_US
dc.identifier.issn1532-0480en_US
dc.identifier.issue2en_US
dc.identifier.pmid23262450en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage328en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jbi.2012.12.002
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29149
dc.identifier.volume46en_US
dc.identifier.wosWOS:000317322500013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEen_US
dc.relation.ispartofJOURNAL OF BIOMEDICAL INFORMATICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSingle Nucleotide Polymorphisms (SNPs)en_US
dc.subjectTag SNPsen_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectSupport vector machine (SVM)en_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.titleA genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPsen_US
dc.typeArticleen_US

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