HIV Drug Resistance Prediction with Categorical Kernel Functions

Abstract

Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. In this paper, we propose the use of categorical kernel functions to predict the resistance to 18 drugs from virus sequence data. These kernel functions are able to take into account HIV data particularities, as are the allele mixtures, and to integrate additional knowledge about the major resistance associated protein positions described in the literature.