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A comparative study on filtering protein secondary structure prediction

dc.contributor.authorKountouris, P.en
dc.contributor.authorAgathocleous, Michalisen
dc.contributor.authorPromponas, Vasilis J.en
dc.contributor.authorChristodoulou, Georgiaen
dc.contributor.authorHadjicostas, S.en
dc.contributor.authorVassiliades, Vassilisen
dc.contributor.authorChristodoulou, Chris C.en
dc.creatorKountouris, P.en
dc.creatorAgathocleous, Michalisen
dc.creatorPromponas, Vasilis J.en
dc.creatorChristodoulou, Georgiaen
dc.creatorHadjicostas, S.en
dc.creatorVassiliades, Vassilisen
dc.creatorChristodoulou, Chris C.en
dc.date.accessioned2019-11-13T10:40:47Z
dc.date.available2019-11-13T10:40:47Z
dc.date.issued2012
dc.identifier.issn1545-5963
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54295
dc.description.abstractFiltering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement. © 2006 IEEE.en
dc.sourceIEEE/ACM Transactions on Computational Biology and Bioinformaticsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84859176544&doi=10.1109%2fTCBB.2012.22&partnerID=40&md5=d955a21ddfc96aeab55e63e5cc36b9a2
dc.subjectarticleen
dc.subjecthumanen
dc.subjectHumansen
dc.subjectproteinen
dc.subjectcomparative studyen
dc.subjectAnimalsen
dc.subjectanimalen
dc.subjectchemistryen
dc.subjectartificial intelligenceen
dc.subjectFiltrationen
dc.subjectRecurrent neural networksen
dc.subjectprotein secondary structureen
dc.subjectBioinformaticsen
dc.subjectProteinsen
dc.subjectprotein databaseen
dc.subjectProtein Structure, Secondaryen
dc.subjectmachine learningen
dc.subjectComparative studiesen
dc.subjectLearning systemsen
dc.subjectDatabases, Proteinen
dc.subjectBidirectional recurrent neural networksen
dc.subjectProtein secondary structure predictionen
dc.subjectMachine learning techniquesen
dc.subjectbidirectional recurrent neural networks.en
dc.subjectfilteringen
dc.subjectMachine-learningen
dc.subjectPredictive performanceen
dc.subjectstructural bioinformaticsen
dc.titleA comparative study on filtering protein secondary structure predictionen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1109/TCBB.2012.22
dc.description.volume9
dc.description.issue3
dc.description.startingpage731
dc.description.endingpage739
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :7</p>en
dc.source.abbreviationIEEE/ACM Trans.Comput.BioL.Bioinf.en
dc.contributor.orcidPromponas, Vasilis J. [0000-0003-3352-4831]
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]
dc.gnosis.orcid0000-0003-3352-4831
dc.gnosis.orcid0000-0001-9398-5256


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