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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/10753
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dc.contributor.authorBlekas, K.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorLikas, A.en
dc.date.accessioned2015-11-24T17:00:21Z-
dc.date.available2015-11-24T17:00:21Z-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/10753-
dc.rightsDefault Licence-
dc.titleProtein sequence classification using probabilistic motifs and neural networksen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2003-
heal.abstractThe basic issue concerning the construction of neural network systems for protein classification is the sequence encoding scheme that must be used in order to feed the network. To deal with this problem we propose a method that maps a protein sequence into a numerical feature space using the matching local scores of the sequence to groups of conserved patterns (called motifs). We consider two alternative schemes for discovering a group of D motifs within a set of K-class sequences. We also evaluate the impact of the background features (2-grams) to the performance of the neural system. Experimental results on real datasets indicate that the proposed method is superior to other known protein classification approaches.en
heal.journalNameArtificail Neural Networks and Neural Information Processing - Ican/Iconip 2003en
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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