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  Mining complex genotypic features for predicting HIV-1 drug resistance

Saigo, H., Uno, T., & Tsuda, K. (2007). Mining complex genotypic features for predicting HIV-1 drug resistance. Bioinformatics, 23(18), 2455-2462. doi:10.1093/bioinformatics/btm353.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CBBB-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-BC6D-0
Genre: Journal Article

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Saigo, H1, 2, Author              
Uno, T, Author
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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. 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.

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 Dates: 2007-09
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1093/bioinformatics/btm353
BibTex Citekey: 4603
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 23 (18) Sequence Number: - Start / End Page: 2455 - 2462 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991