Mutation-based Binary Aquila optimizer for gene selection in cancer classification

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dc.authoridhttps://orcid.org/0000-0001-7401-4964en_US
dc.contributor.authorPashaei, Elham
dc.date.accessioned2023-11-01T18:34:53Z
dc.date.available2023-11-01T18:34:53Z
dc.date.issued2022en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractMicroarray data classification is one of the hottest issues in the field of bioinformatics due to its efficiency in diagnosing patients’ ailments. But the difficulty is that microarrays possess a huge number of genes where the majority of which are redundant or irrelevant resulting in the deterioration of classification accuracy. For this issue, mutated binary Aquila Optimizer (MBAO) with a time-varying mirrored S-shaped (TVMS) transfer function is proposed as a new wrapper gene (or feature) selection method to find the optimal subset of informative genes. The suggested hybrid method utilizes Minimum Redundancy Maximum Relevance (mRMR) as a filtering approach to choose top-ranked genes in the first stage and then uses MBAO-TVMS as an efficient wrapper approach to identify the most discriminative genes in the second stage. TVMS is adopted to transform the continuous version of Aquila Optimizer (AO) to binary one and a mutation mechanism is incorporated into binary AO to aid the algorithm to escape local optima and improve its global search capabilities. The suggested method was tested on eleven well-known benchmark microarray datasets and compared to other current state-ofthe-art methods. Based on the obtained results, mRMR-MBAO confirms its superiority over the mRMR-BAO algorithm and the other comparative GS approaches on the majority of the medical datasets strategies in terms of classification accuracy and the number of selected genes. R codes of MBAO are available at https://github. com/el-pashaei/MBAO.en_US
dc.identifier.doi10.1016/j.compbiolchem.2022.107767en_US
dc.identifier.endpage16en_US
dc.identifier.issn1476-9271
dc.identifier.issn1476-928X
dc.identifier.pmid36084602en_US
dc.identifier.scopus2-s2.0-85137270922en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/6170
dc.identifier.urihttps://doi.org/
dc.identifier.volume101en_US
dc.identifier.wosWOS:000858873400004en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPashaei, Elham
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDen_US
dc.relation.ispartofComputational Biology and Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectCancer classificationen_US
dc.subjectFeature selectionen_US
dc.subjectAquila optimizeren_US
dc.subjectOptimizationen_US
dc.subjectMutationen_US
dc.titleMutation-based Binary Aquila optimizer for gene selection in cancer classificationen_US
dc.typeArticleen_US

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