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Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies

Author

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  • Bogojeska Jasmina

    (Max-Planck Institute for Informatics, Saarbrücken, Germany)

  • Lengauer Thomas

    (Max-Planck Institute for Informatics, Saarbrücken, Germany)

Abstract

HIV patients are treated by administration of combinations of antiretroviral drugs. The very large number of such combinations makes the manual search for an effective therapy practically impossible, especially in advanced stages of the disease. Therapy selection can be supported by statistical methods that predict the outcomes of candidate therapies. However, these methods are based on clinical data sets that have highly unbalanced therapy representation.This paper presents a novel approach that considers each drug belonging to a target combination therapy as a separate task in a multi-task hierarchical Bayes setting. The drug-specific models take into account information on all therapies containing the drug, not just the target therapy. In this way, we can circumvent the problem of data sparseness pertaining to some target therapies.The computational validation shows that compared to the most commonly used approach that provides therapy information in the form of input features, our model has significantly higher predictive power for therapies with very few training samples and is at least as powerful for abundant therapies.

Suggested Citation

  • Bogojeska Jasmina & Lengauer Thomas, 2012. "Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-21, April.
  • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:3:n:11
    DOI: 10.1515/1544-6115.1769
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    References listed on IDEAS

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    1. Richard H. Lathrop & Michael J. Pazzani, 1999. "Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses," Journal of Combinatorial Optimization, Springer, vol. 3(2), pages 301-320, July.
    2. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
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    Cited by:

    1. Verena Schildgen & Ilija Nenadic & Michael Brockmann & Oliver Schildgen, 2017. "Diagnostics for Targeted NSCLC Therapy," Challenges, MDPI, vol. 8(2), pages 1-6, November.

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