Automated prediction of HIV drug resistance from genotype data.

ABSTRACT:

Background

HIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens.

Results

A unified encoding of protein sequence and structure was used as the feature vector for predicting phenotypic resistance from genotype data. Two machine learning algorithms, Random Forest and K-nearest neighbor, were used. The prediction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets. A supervised machine learning approach for automatic prediction of drug resistance was developed to handle genotype-phenotype datasets of HIV prot

SUBMITTER: Shen C 

PROVIDER: S-EPMC5009519 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature