Feature Selection for Protein Dihedral Angle Prediction

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dc.contributor.authorAydin, Zafer
dc.contributor.authorKaynar, Oguz
dc.contributor.authorGormez, Yasin
dc.contributor.editorKoyuncu, B
dc.contributor.editorTomar, GS
dc.date.accessioned2019-07-27T12:10:23Z
dc.date.accessioned2019-07-28T09:44:05Z
dc.date.available2019-07-27T12:10:23Z
dc.date.available2019-07-28T09:44:05Z
dc.date.issued2017
dc.department[Aydin, Zafer] Abdullah Gul Univ, Kayseri, Turkey -- [Kaynar, Oguz -- Gormez, Yasin] Cumhuriyet Univ, Sivas, Turkeyen_US
dc.description9th International Conference on Computational Intelligence and Communication Networks (CICN) -- SEP 16-17, 2017 -- Final Int Univ, Girne, CYPRUSen_US
dc.description.abstractThree-dimensional structure prediction has crucial importance for bioinformatics and theoretical chemistry. One of the main steps of three-dimensional structure prediction is dihedral (torsion) angle prediction. As new feature extraction methods are developed the dimension of the input space increases considerably yielding longer model training and less accurate models due to noisy or redundant features. In this study, feature selection is employed for dimensionality reduction on one of the established benchmarks of protein 1D structure prediction. Experimental results show that the feature selection improves the accuracy of protein dihedral angle class prediction by 2% and can eliminate up to %82 of the features when random forest classifier is used. Accurate prediction of dihedral angles will eventually contribute to protein structure prediction.en_US
dc.description.sponsorshipMIR Labs, IEEE Turkey Secten_US
dc.description.sponsorship3501 TUBITAK National Young Researchers Career Award [113E550]en_US
dc.description.sponsorshipThis work is supported by grant 113E550 from 3501 TUBITAK National Young Researchers Career Award.en_US
dc.identifier.doi10.1109/CICN.2017.13en_US
dc.identifier.endpage52en_US
dc.identifier.isbn978-1-5090-5001-7
dc.identifier.issn2375-8244
dc.identifier.scopus2-s2.0-85050860094en_US
dc.identifier.scopusqualityN/A
dc.identifier.startpage48en_US
dc.identifier.urihttps://dx.doi.org/10.1109/CICN.2017.13
dc.identifier.urihttps://hdl.handle.net/20.500.12418/6900
dc.identifier.wosWOS:000432249700011en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN)en_US
dc.relation.ispartofseriesInternational Confernce on Computational Intelligence and Communication Networks
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfeature selectionen_US
dc.subjectprotein structure predictionen_US
dc.subjectdihedral angle predictionen_US
dc.subjectbackbone angleen_US
dc.subjectrandom foresten_US
dc.titleFeature Selection for Protein Dihedral Angle Predictionen_US
dc.typeConference Objecten_US

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