| pubmed-article:18586740 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C0017428 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C1521761 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C1510425 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C0205210 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C1705938 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C2346866 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C1527178 | lld:lifeskim |
| pubmed-article:18586740 | lifeskim:mentions | umls-concept:C1883525 | lld:lifeskim |
| pubmed-article:18586740 | pubmed:issue | 13 | lld:pubmed |
| pubmed-article:18586740 | pubmed:dateCreated | 2008-6-30 | lld:pubmed |
| pubmed-article:18586740 | pubmed:abstractText | Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. | lld:pubmed |
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| pubmed-article:18586740 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
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| pubmed-article:18586740 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18586740 | pubmed:commentsCorrections | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18586740 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18586740 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18586740 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18586740 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18586740 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18586740 | pubmed:month | Jul | lld:pubmed |
| pubmed-article:18586740 | pubmed:issn | 1367-4811 | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:ZazziMaurizio... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:KaiserRolfR | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:SönnerborgAnd... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:LengauerThoma... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:Rosen-ZviMich... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:IncardonaFran... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:NeuvirthHaniH | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:AltmannAndreA | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:ProsperiMatti... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:StruckDanielD | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:AharoniEhudE | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:SchülterEugen... | lld:pubmed |
| pubmed-article:18586740 | pubmed:author | pubmed-author:PeresYardenaY | lld:pubmed |
| pubmed-article:18586740 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:18586740 | pubmed:day | 1 | lld:pubmed |
| pubmed-article:18586740 | pubmed:volume | 24 | lld:pubmed |
| pubmed-article:18586740 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18586740 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18586740 | pubmed:pagination | i399-406 | lld:pubmed |
| pubmed-article:18586740 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:meshHeading | pubmed-meshheading:18586740... | lld:pubmed |
| pubmed-article:18586740 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18586740 | pubmed:articleTitle | Selecting anti-HIV therapies based on a variety of genomic and clinical factors. | lld:pubmed |
| pubmed-article:18586740 | pubmed:affiliation | Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com | lld:pubmed |
| pubmed-article:18586740 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:18586740 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:18586740 | lld:pubmed |
