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Predicting miRNA targets utilizing an extended profile HMM

dc.contributor.authorGrau, Jan
dc.contributor.authorArend, Daniel
dc.contributor.authorGrosse, Ivo
dc.contributor.authorHatzigeorgiou, Artemis G.
dc.contributor.authorKeilwagen, Jens
dc.contributor.authorMaragkakis, Manolis
dc.contributor.authorWeinholdt, Claus
dc.contributor.authorPosch, Stefan
dc.contributor.editorSchomburg, Dietmar
dc.contributor.editorGrote, Andreas
dc.date.accessioned2019-01-17T10:57:30Z
dc.date.available2019-01-17T10:57:30Z
dc.date.issued2010
dc.description.abstractThe regulation of many cellular processes is influenced by miRNAs, and bioinformatics approaches for predicting miRNA targets evolve rapidly. Here, we propose conditional profile HMMs that learn rules of miRNA-target site interaction automatically from data. We demonstrate that conditional profile HMMs detect the rules implemented into existing approaches from their predictions. And we show that a simple UTR model utilizing conditional profile HMMs predicts target genes of miR- NAs with a precision that is competitive compared to leading approaches, although it does not exploit cross-species conservation.en
dc.identifier.isbn978-3-88579-267-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/19675
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman Conference on Bioinformatics 2010
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-173
dc.titlePredicting miRNA targets utilizing an extended profile HMMen
dc.typeText/Conference Paper
gi.citation.endPage91
gi.citation.publisherPlaceBonn
gi.citation.startPage81
gi.conference.dateSeptember 20-22, 2010
gi.conference.locationBraunschweig
gi.conference.sessiontitleRegular Research Papers

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