Knowledge-based avoidance of drug-resistant HIV mutants
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Arevir: a secure platform for designing personalized antiretroviral therapies against HIV
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Modelling, aggregation and simulation of a dynamic biological system through fuzzy cognitive maps
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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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.