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ABSTRACT
<p>Rapid accumulation of resistance mutations in the genome of the human immunodeficiency virus (HIV) plays a central role in drug treatment failure in infected patients. The authors have developed geno2pheno, an intelligent system that uses the information encoded in the viral genomic sequence to predict resistance or susceptibility of the virus to 13 antiretroviral agents. To predict phenotypic drug resistance from genotype, they applied two machine learning techniques: decision trees and linear support vector machines. These techniques performed learning on more than 400 genotype-phenotype pairs for each drug. The authors compared the generalization performance of the two families of models in leave-one-out experiments. Except for three drugs, all error estimates ranged between 7.25 and 15.5 percent. Support vector machines performed slightly better for most drugs, but knowledge extraction was easier for decision trees. Geno2pheno is freely available at http://cartan.gmd.de/geno2pheno.html.</p>
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CITATION
Klaus Korn, Thomas Lengauer, Barbara Schmidt, Joachim Selbig, Daniel Hoffman, Rolf Kaiser, Niko Beerenwinkel, Hauke Walter, "Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests", IEEE Intelligent Systems, vol. 16, no. , pp. 35-41, November/December 2001, doi:10.1109/5254.972080
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