|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:issue |
10
|
|
pubmed:dateCreated |
2008-10-22
|
|
pubmed:abstractText |
Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers.
|
|
pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-12824435,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-15871130,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-16108705,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-16980939,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-17402922,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-17503659,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-17503743,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-17720996,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-18586740,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-18677028,
http://linkedlifedata.com/resource/pubmed/commentcorrection/18941628-7541846
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:chemical |
|
|
pubmed:status |
MEDLINE
|
|
pubmed:issn |
1932-6203
|
|
pubmed:author |
pubmed-author:AharoniEhudE,
pubmed-author:AltmannAndréA,
pubmed-author:BüchJoachimJ,
pubmed-author:IncardonaFrancescaF,
pubmed-author:KaiserRolfR,
pubmed-author:LengauerThomasT,
pubmed-author:NeuvirthHaniH,
pubmed-author:PeresYardenaY,
pubmed-author:ProsperiMattiaM,
pubmed-author:Rosen-ZviMichalM,
pubmed-author:SönnerborgAndersA,
pubmed-author:SchülterEugenE,
pubmed-author:StruckDanielD,
pubmed-author:ZazziMaurizioM
|
|
pubmed:issnType |
Electronic
|
|
pubmed:volume |
3
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
e3470
|
|
pubmed:dateRevised |
2009-11-18
|
|
pubmed:meshHeading |
pubmed-meshheading:18941628-Anti-HIV Agents,
pubmed-meshheading:18941628-Artificial Intelligence,
pubmed-meshheading:18941628-Computational Biology,
pubmed-meshheading:18941628-Diagnosis, Computer-Assisted,
pubmed-meshheading:18941628-Drug Resistance,
pubmed-meshheading:18941628-Genome, Viral,
pubmed-meshheading:18941628-Genotype,
pubmed-meshheading:18941628-Internet,
pubmed-meshheading:18941628-Methods,
pubmed-meshheading:18941628-Models, Statistical,
pubmed-meshheading:18941628-Mutation
|
|
pubmed:year |
2008
|
|
pubmed:articleTitle |
Comparison of classifier fusion methods for predicting response to anti HIV-1 therapy.
|
|
pubmed:affiliation |
Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany. altmann@mpi-inf.mpg.de
|
|
pubmed:publicationType |
Journal Article,
Comparative Study,
Research Support, Non-U.S. Gov't
|
