title: Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.
abstract: OBJECTIVE
  Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries.
  
  
  METHODS
  Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database.
  
  
  RESULTS
  Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations.
  
  
  CONCLUSIONS
  MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors.
authors: Computational Intelligence Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Boadilla del Monte 28660, Spain. hanen.borchani@upm.es
authors: Hanen
authors: H
authors: Borchani
authors: Concha
authors: C
authors: Bielza
authors: Carlos
authors: C
authors: Toro
authors: Pedro
authors: P
authors: Larrañaga
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D001499:Bayes Theorem; D006678:HIV / Q000187:drug effects*; D008390:Markov Chains
chemical_list: D019380:Anti-HIV Agents