Prediction of E.Coli promoter gene sequences using a hybrid combination based on feature selection, fuzzy weighted pre-processing, and decision tree classifier

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorPolat, Kemal
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:17:56Z
dc.date.available2020-03-26T17:17:56Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks -- SEP 12-14, 2007 -- Vietri sul Mare, ITALYen_US
dc.description.abstractIn this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a hybrid approach based on combining feature selection (FS), fuzzy weighted preprocessing, and C4.5 decision tree classifier (DCS). Dimensionality of E.coli Promoter Gene Sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed approach consists of three stages. Firstly, we have used the FS process to reduce the dimensionality of E.coli Promoter Gene Sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. Secondly, fuzzy weighted pre-processing has been used to weight E.coli Promoter Gene Sequences dataset that has 4 attributes in interval of [0,1]. Finally, C4.5 decision tree classifier algorithm has been run to estimation the E.coli Promoter Gene Sequences. In order to show the performance of the proposed system, we have used the predicton accuracy and 10-fold cross validation. 93.33% classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in the prediction of E.coli Promoter Gene Sequences.en_US
dc.description.sponsorshipUniv Studi Milano, Second Univ Naples, Comune Vietri Mare, Comune Salerno, Reg Campania, Minist Riforme Innovaz nella P A, Ctr Reg Informat Commun Technolen_US
dc.description.sponsorshipScientific Research Project of Selcuk UniversitySelcuk University [05401069]en_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University (Project No: 05401069).en_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-540-74817-5
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage125en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21541
dc.identifier.volume4692en_US
dc.identifier.wosWOS:000250338500016en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofKNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT I, PROCEEDINGSen_US
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titlePrediction of E.Coli promoter gene sequences using a hybrid combination based on feature selection, fuzzy weighted pre-processing, and decision tree classifieren_US
dc.typeConference Objecten_US

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