dc.contributor.author | Aydin, Zafer | |
dc.contributor.author | Kaynar, Oguz | |
dc.contributor.author | Gormez, Yasin | |
dc.contributor.editor | Koyuncu, B | |
dc.contributor.editor | Tomar, GS | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T09:44:05Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T09:44:05Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 978-1-5090-5001-7 | |
dc.identifier.issn | 2375-8244 | |
dc.identifier.uri | https://dx.doi.org/10.1109/CICN.2017.13 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/6900 | |
dc.description | 9th International Conference on Computational Intelligence and Communication Networks (CICN) -- SEP 16-17, 2017 -- Final Int Univ, Girne, CYPRUS | en_US |
dc.description | WOS: 000432249700011 | en_US |
dc.description.abstract | Three-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.sponsorship | MIR Labs, IEEE Turkey Sect | en_US |
dc.description.sponsorship | 3501 TUBITAK National Young Researchers Career Award [113E550] | en_US |
dc.description.sponsorship | This work is supported by grant 113E550 from 3501 TUBITAK National Young Researchers Career Award. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | International Confernce on Computational Intelligence and Communication Networks | |
dc.relation.isversionof | 10.1109/CICN.2017.13 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | feature selection | en_US |
dc.subject | protein structure prediction | en_US |
dc.subject | dihedral angle prediction | en_US |
dc.subject | backbone angle | en_US |
dc.subject | random forest | en_US |
dc.title | Feature Selection for Protein Dihedral Angle Prediction | en_US |
dc.type | conferenceObject | en_US |
dc.relation.journal | 2017 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN) | en_US |
dc.contributor.department | [Aydin, Zafer] Abdullah Gul Univ, Kayseri, Turkey -- [Kaynar, Oguz -- Gormez, Yasin] Cumhuriyet Univ, Sivas, Turkey | en_US |
dc.identifier.endpage | 52 | en_US |
dc.identifier.startpage | 48 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |