A new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier system

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dc.contributor.authorPolat, Kemal
dc.contributor.authorGunes, Salih
dc.date.accessioned2020-03-26T17:37:44Z
dc.date.available2020-03-26T17:37:44Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractWe have investigated the real-world task of recognizing biological concepts in DNA sequences in this work. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a novel approach based on feature selection (FS) and Artificial Immune Recognition System (AIRS) with Fuzzy resource allocation mechanism (Fuzzy-AIRS), which is. first proposed by us. The aim of this study is to improve the prediction accuracy of Escherichia coli promoter gene sequences using a novel system based on FS and Fuzzy-AIRS. The E. coli promoter gene sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed system consists of two parts. Firstly, we have reduced the dimension of E. coli promoter gene sequences dataset from 57 attributes to 4 attributes by means of FS process. Second, Fuzzy-AIRS classifier algorithm has been run to predict the E. coli promoter gene sequences. The robustness of the proposed method is examined using prediction accuracy, sensitivity and specificity analysis, k-fold cross-validation method and confusion matrix. Whilst only Fuzzy-AIRS classifier has obtained 50% prediction accuracy using 10-fold cross-validation, the proposed system has obtained 90% prediction accuracy in the same conditions. These obtained results have indicated that the proposed system obtain the success rate in recognizing promoters in strings that represent nucleotides. (C) 2007 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipScientific Research Projects of Selcuk UniversitySelcuk University [05401069]en_US
dc.description.sponsorshipThis study is supported by the Scientific Research Projects of Selcuk University (Project No. 05401069).en_US
dc.identifier.doi10.1016/j.eswa.2007.09.010en_US
dc.identifier.endpage64en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage57en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.09.010
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23219
dc.identifier.volume36en_US
dc.identifier.wosWOS:000264182800006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectEscherichia coli promoter gene sequencesen_US
dc.subjectFeature selectionen_US
dc.subjectArtificial immune systemen_US
dc.subjectAIRS classification systemen_US
dc.subjectFuzzy resource allocation mechanismen_US
dc.subject10-fold cross-validationen_US
dc.subjectPredictionen_US
dc.titleA new method to forecast of Escherichia coli promoter gene sequences: Integrating feature selection and Fuzzy-AIRS classifier systemen_US
dc.typeArticleen_US

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