title: Selecting anti-HIV therapies based on a variety of genomic and clinical factors.
abstract: BACKGROUND
  Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy.
  
  
  RESULTS
  Three different machine learning techniques were used: generative-discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome.
  
  
  BACKGROUND
  The combined prediction engine will be available from July 2008, see http://engine.euresist.org.
authors.affiliation: Machine learning group, IBM Research Laboratory in Haifa, Israel. rosen@il.ibm.com
authors.forename: Michal
authors.initials: M
authors.lastname: Rosen-Zvi
authors.forename: Andre
authors.initials: A
authors.lastname: Altmann
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.forename: Ehud
authors.initials: E
authors.lastname: Aharoni
authors.forename: Hani
authors.initials: H
authors.lastname: Neuvirth
authors.forename: Anders
authors.initials: A
authors.lastname: Sönnerborg
authors.forename: Eugen
authors.initials: E
authors.lastname: Schülter
authors.forename: Daniel
authors.initials: D
authors.lastname: Struck
authors.forename: Yardena
authors.initials: Y
authors.lastname: Peres
authors.forename: Francesca
authors.initials: F
authors.lastname: Incardona
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
mesh_terms: D019380:Anti-HIV Agents / Q000627:therapeutic use*; D002874:Chromosome Mapping / Q000379:methods*; D020000:Decision Support Systems, Clinical; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics*; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010597:Pharmacogenetics / Q000379:methods*
chemical_list: D019380:Anti-HIV Agents