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dc.contributor.authorMC LYSAGHT, AOIFE
dc.date.accessioned2010-05-17T13:39:33Z
dc.date.available2010-05-17T13:39:33Z
dc.date.issued2005
dc.date.submitted2005en
dc.identifier.citationPollastri, G. and McLysaght, A., Porter: a new, accurate server for protein secondary structure prediction, Bioinformatics, 21, 8, 2005, 1719, 1720en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/39594
dc.descriptionPUBLISHEDen
dc.description.abstractPorter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date. AVAILABILITY: Porter is available as a public web server at http://distill.ucd.ie/porter/en
dc.format.extent1719en
dc.format.extent1720en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.ispartofseriesBioinformatics;
dc.relation.ispartofseries21;
dc.relation.ispartofseries8;
dc.rightsYen
dc.subjectGenetics
dc.titlePorter: a new, accurate server for protein secondary structure predictionen
dc.typeJournal Articleen
dc.contributor.sponsorScience Foundation Ireland
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/mclysaga
dc.identifier.rssinternalid12149
dc.identifier.rssurihttp://bioinformatics.oupjournals.org/cgi/content/abstract/bti203?ijkey=021zKGAa7MMWA&keytype=refen


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