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Bioinformatics Advance Access originally published online on August 13, 2007
Bioinformatics 2007 23(18):2455-2462; doi:10.1093/bioinformatics/btm353
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Mining complex genotypic features for predicting HIV-1 drug resistance

Hiroto Saigo 1, Takeaki Uno 2 and Koji Tsuda 1,*

1Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany and 2National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, Japan

*To whom correspondence should be addressed.


  Abstract

Motivation: Human immunodeficiency virus type 1 (HIV-1) evolves in human body, and its exposure to a drug often causes mutations that enhance the resistance against the drug. To design an effective pharmacotherapy for an individual patient, it is important to accurately predict the drug resistance based on genotype data. Notably, the resistance is not just the simple sum of the effects of all mutations. Structural biological studies suggest that the association of mutations is crucial: even if mutations A or B alone do not affect the resistance, a significant change might happen when the two mutations occur together. Linear regression methods cannot take the associations into account, while decision tree methods can reveal only limited associations. Kernel methods and neural networks implicitly use all possible associations for prediction, but cannot select salient associations explicitly.

Results: Our method, itemset boosting, performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation combination is found by an efficient branch-and-bound search. This method uses all possible combinations, and salient associations are explicitly shown. In experiments, our method worked particularly well for predicting the resistance of nucleotide reverse transcriptase inhibitors (NRTIs). Furthermore, it successfully recovered many mutation associations known in biological literature.

Availability: http://www.kyb.mpg.de/bs/people/hiroto/iboost/

Contact: koji.tsuda{at}tuebingen.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Limsoon Wong

Received on May 4, 2007; revised on June 26, 2007; accepted on June 29, 2007

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