Super learning: An application to the prediction of HIV-1 drug resistance

Sandra E. Sinisi, Eric C. Polley, Maya L. Petersen, Soo Yon Rhee, Mark J. Van Der Laan

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations.

Original languageEnglish (US)
Article number7
Pages (from-to)1-24
Number of pages24
JournalStatistical Applications in Genetics and Molecular Biology
Volume6
Issue number1
DOIs
StatePublished - Feb 23 2007

Keywords

  • Antiretroviral
  • Cross-validation
  • Genomics
  • Loss-based estimation
  • Machine learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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