title: 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 protease (PR) and reverse transcriptase (RT). It predicts the drug resistance phenotype and its relative severity from a query sequence. The accuracy of the classification was higher than 0.973 for eight PR inhibitors and 0.986 for ten RT inhibitors, respectively. The overall cross-validated regression R(2)-values for the severity of drug resistance were 0.772-0.953 for 8 PR inhibitors and 0.773-0.995 for 10 RT inhibitors.
  
  
  CONCLUSIONS
  Machine learning using a unified encoding of sequence and protein structure as a feature vector provides an accurate prediction of drug resistance from genotype data. A practical webserver for clinicians has been implemented.
authors: Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.
authors: ChenHsiang
authors: C
authors: Shen
authors: Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA.
authors: Xiaxia
authors: X
authors: Yu
authors: Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.|Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA.
authors: Robert W
authors: RW
authors: Harrison
authors: Department of Biology, Georgia State University, Atlanta, GA, 30303, USA. iweber@gsu.edu.
authors: Irene T
authors: IT
authors: Weber
mesh_terms: D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology; D001331:Automation; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D024882:Drug Resistance, Viral / Q000187:drug effects / Q000235:genetics*; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy; D016333:HIV Protease / Q000235:genetics; D017320:HIV Protease Inhibitors / Q000494:pharmacology; D054303:HIV Reverse Transcriptase / Q000037:antagonists & inhibitors* / Q000737:chemistry; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics*; D006801:Humans; D018894:Reverse Transcriptase Inhibitors / Q000494:pharmacology
chemical_list: D019380:Anti-HIV Agents; D017320:HIV Protease Inhibitors; D018894:Reverse Transcriptase Inhibitors; D054303:HIV Reverse Transcriptase; D016333:HIV Protease
keywords: Automation; Drug resistance prediction; Encoding structure and sequence; HIV/AIDS drugs; Supervised machine learning