Predicting of HIV drug resistance with support vector machines

The purpose of this study was to investigate the performance of SVMs in predicting the drug resistance of HIV, using amino acids sequence analysis. support vector machines were used in this study. LIBSVM software was used to train and test SVMs. This paper presents the performance of SVMs in predicting the drug resistance of HIV, using amino acids sequence analysis. The results of four algorithms (HIVDB, ANRS, REGA and VGI) have been developed to interpret results of HIV genotypic resistance tests, are explored. The most efficient method for each drug is determined. Also, it is indicated that SVMs are a highly successful classifier with accuracy between 86.27- 98.77, for predicting of HIV drug resistance. Using SVMs for HIVDB method results, has best performance for APV, NFV, ABC, AZT, D4T, DDI, TDF and DLV drugs. Using SVMs for ANRS method results has best performance for IDV, 3TC, TDF, EFV and NVP drugs. Using SVMs for REGA method results has best performance for LPV and AZT drugs and using SVMs for VGI method results has best performance for IDV, LPV,RTV, SQV and DDI. The results show that SVMs are a highly successful classifier for predicting of HIV drug resistance. Before starting treatment with each drug, one can determine HIV drug resistance with machine learning methods.