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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

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1544-6115
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Volume 11, Issue 3

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Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies

Jasmina Bogojeska / Thomas Lengauer
Published Online: 2012-04-27 | DOI: https://doi.org/10.1515/1544-6115.1769

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.

Keywords: hierarchical Bayes modelling; HIV combination therapies; statistical models; classification

About the article

Published Online: 2012-04-27


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 11, Issue 3, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1769.

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