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Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy

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Listed:
  • Saigo Hiroto

    (Max Planck Institute for Informatics)

  • Altmann Andre

    (Max Planck Institute for Informatics)

  • Bogojeska Jasmina

    (Max Planck Institute for Informatics)

  • Müller Fabian

    (Max Planck Institute for Informatics)

  • Nowozin Sebastian

    (Max Planck Institute for Biological Cybernetics)

  • Lengauer Thomas

    (Max Planck Institute for Informatics)

Abstract

Infections with the human immunodeficiency virus type 1 (HIV-1) are treated with combinations of drugs. Unfortunately, HIV responds to the treatment by developing resistance mutations. Consequently, the genome of the viral target proteins is sequenced and inspected for resistance mutations as part of routine diagnostic procedures for ensuring an effective treatment. For predicting response to a combination therapy, currently available computer-based methods rely on the genotype of the virus and the composition of the regimen as input. However, no available tool takes full advantage of the knowledge about the order of and the response to previously prescribed regimens. The resulting high-dimensional feature space makes existing methods difficult to apply in a straightforward fashion. The machine learning system proposed in this work, sequence boosting, is tailored to exploiting such high-dimensional information, i.e. the extraction of longitudinal features, by utilizing the recent advancements in data mining and boosting.

Suggested Citation

  • Saigo Hiroto & Altmann Andre & Bogojeska Jasmina & Müller Fabian & Nowozin Sebastian & Lengauer Thomas, 2011. "Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-32, January.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:6
    DOI: 10.2202/1544-6115.1604
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    References listed on IDEAS

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    1. Foulkes A.S. & De Gruttola V., 2003. "Characterizing the Progression of Viral Mutations Over Time," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 859-867, January.
    2. A. S. Foulkes & V. De Gruttola, 2002. "Characterizing the Relationship Between HIV-1 Genotype and Phenotype: Prediction-Based Classification," Biometrics, The International Biometric Society, vol. 58(1), pages 145-156, March.
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