de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44156

Bogojeska,  Jasmina
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44907

Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Bogojeska, J., & Lengauer, T. (2012). Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies. Statistical Applications in Genetics and Molecular Biology, 11(3): 11, pp. 1-19. doi:10.1515/1544-6115.1769.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-C580-C
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.