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Semi-supervised learning for improving prediction of HIV drug resistance

dc.contributor.authorPerner, Juliane
dc.contributor.authorAltmann, André.
dc.contributor.authorLengauer, Thomas
dc.contributor.editorGrosse, Ivo
dc.contributor.editorNeumann, Steffen
dc.contributor.editorPosch, Stefan
dc.contributor.editorSchreiber, Falk
dc.contributor.editorStadler, Peter
dc.date.accessioned2019-02-20T09:48:31Z
dc.date.available2019-02-20T09:48:31Z
dc.date.issued2009
dc.description.abstractResistance testing is an important tool in today's anti-HIV therapy management for improving the success of antiretroviral therapy. Routinely, the genetic sequence of viral target proteins is obtained. These sequences are then inspected for mutations that might confer resistance to antiretroviral drugs. However, interpretation of the genomic data is challenging. In recent years, approaches that employ supervised statistical learning methods were made available to assist the interpretation of the complex genetic information (e.g. geno2pheno and VircoTYPE). However, these methods rely on large amounts of labeled training data, which are expensive and labor-intensive to obtain. This work evaluates the application of semi-supervised learning (SSL) for improving the prediction of resistance from the viral genome.en
dc.identifier.isbn978-3-88579-251-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/20310
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman conference on bioinformatics 2009
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-157
dc.titleSemi-supervised learning for improving prediction of HIV drug resistanceen
dc.typeText/Conference Paper
gi.citation.endPage65
gi.citation.publisherPlaceBonn
gi.citation.startPage55
gi.conference.date28th to 30th September 2009
gi.conference.locationHalle-Wittenberg
gi.conference.sessiontitleRegular Research Papers

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