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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. Poster presented at 15th Annual International Conference on Intelligent Systems for Molecular Biology & 6th European Conference on Computational Biology (ISMB/ECCB 2007), Wien, Austria.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-01AA-B Version Permalink: http://hdl.handle.net/21.11116/0000-0004-01AB-A
Genre: Poster

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 Creators:
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, ou_1497794              

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 Abstract: To design an effective pharmacotherapy for HIV-1 patients, it is important to consider mutation associations in the genotypic data. Our method, item set boosting, performs linear regression in the complete space of power sets of mutations. In computational experiments our method succeeded in recovering salient associations which explain drug resistance.

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 Dates: 2007-07
 Publication Status: Published online
 Pages: -
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Title: 15th Annual International Conference on Intelligent Systems for Molecular Biology & 6th European Conference on Computational Biology (ISMB/ECCB 2007)
Place of Event: Wien, Austria
Start-/End Date: 2007-07-21 - 2007-07-25

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