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A Data Fusion Approach In Protein Homology Detection

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dc.contributor.author Polatkan, Aydın Can
dc.contributor.author Oğul, Hasan
dc.contributor.author Sever, Hayri
dc.date.accessioned 2020-04-27T21:05:32Z
dc.date.available 2020-04-27T21:05:32Z
dc.date.issued 2008
dc.identifier.citation Sever, Hayri; Polatkan, Aydin Can; Ogul, Hasan, "A Data Fusion Approach In Protein Homology Detection", Proceedings - International Conference On Biocomputation, Bioinformatics, and Biomedical Technologies, Bıotechno 2008, pp. 7-12, (2008). tr_TR
dc.identifier.issn 978-076953191-5
dc.identifier.uri http://hdl.handle.net/20.500.12416/3463
dc.description.abstract The discriminative framework for protein remote homology detection based on support vector machines (SVMs) is reconstructed by the fusion of sequence based features. In this respect, n-peptide compositions are partitioned and fed into separate SVMs. The SVM outputs are evaluated with different techniques and tested to discern their ability for SCOP protein super family classification on a common benchmarking set. It reveals that the fusion approach leads to an improvement in prediction accuracy with a remarkable gain on computer memory usage. © 2008 IEEE. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE tr_TR
dc.relation.isversionof 10.1109/BIOTECHNO.2008.23 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Bioinformatics tr_TR
dc.subject Proteins tr_TR
dc.subject Remote Homology tr_TR
dc.title A Data Fusion Approach In Protein Homology Detection tr_TR
dc.type bookPart tr_TR
dc.relation.journal Proceedings - International Conference On Biocomputation, Bioinformatics, and Biomedical Technologies, Bıotechno 2008 tr_TR
dc.contributor.authorID 11916 tr_TR
dc.identifier.startpage 7 tr_TR
dc.identifier.endpage 12 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


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