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dc.contributor.authorSaraç, Ö. S.en_US
dc.contributor.authorAtalay, V.en_US
dc.contributor.authorÇetin-Atalay, Rengülen_US
dc.coverage.spatialAntalya, Turkeyen_US
dc.date.accessioned2016-02-08T11:46:39Z
dc.date.available2016-02-08T11:46:39Z
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/11693/27176
dc.descriptionDate of Conference: 17-19 April 2006en_US
dc.descriptionConference Name: IEEE 14th Signal Processing and Communications Applications Conference, SIU 2006en_US
dc.description.abstractRepresentation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins.en_US
dc.language.isoTurkishen_US
dc.source.titleProceedings of the IEEE 14th Signal Processing and Communications Applications Conference, SIU 2006en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2006.1659859en_US
dc.subjectPrimary sequencesen_US
dc.subjectProtein sequencesen_US
dc.subjectStructural classesen_US
dc.subjectAlgorithmsen_US
dc.subjectAmino acidsen_US
dc.subjectClassification (of information)en_US
dc.subjectCluster analysisen_US
dc.subjectHidden Markov modelsen_US
dc.subjectOptimizationen_US
dc.subjectProteinsen_US
dc.subjectFeature extractionen_US
dc.titleA novel model-based method for feature extraction from protein sequences for classificationen_US
dc.title.alternativeSınıflandırma için protein dizilerinin özniteliklerinin çıkarılmasında model tabanlı yeni bir yöntemen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.citation.volumeNumber2006en_US
dc.identifier.doi10.1109/SIU.2006.1659859en_US
dc.publisherIEEEen_US


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