Sequence-based protein structure prediction using a reduced state-space hidden Markov model

dc.contributor.authorLampros, C.en
dc.contributor.authorPapaloukas, C.en
dc.contributor.authorExarchos, T. P.en
dc.contributor.authorGoletsis, Y.en
dc.contributor.authorFotiadis, D. I.en
dc.date.accessioned2015-11-24T17:38:10Z
dc.date.available2015-11-24T17:38:10Z
dc.identifier.issn0010-4825-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/14453
dc.rightsDefault Licence-
dc.subjectstructure predictionen
dc.subjectfold recognitionen
dc.subjecthidden markov modelsen
dc.subjectprotein classificationen
dc.subjectsupport vector machinesen
dc.subjectfold recognitionen
dc.subjectsecondary structureen
dc.subjectneural networksen
dc.subjectalignmenten
dc.titleSequence-based protein structure prediction using a reduced state-space hidden Markov modelen
heal.abstractThis work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast. (C) 2006 Elsevier Ltd. All rights reserved.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primaryDOI 10.1016/j.compbiomed.2006.10.014-
heal.identifier.secondary<Go to ISI>://000249489700001-
heal.identifier.secondaryhttp://ac.els-cdn.com/S0010482506001995/1-s2.0-S0010482506001995-main.pdf?_tid=0083e658825388d107524658e307b1b7&acdnat=1339758375_2ae10221c65f7d088804401f69608181-
heal.journalNameComput Biol Meden
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2007-
heal.publisherElsevieren
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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