Mining complex genotypic features for predicting HIV-1 drug resistance

Saigo, H; Uno, T; Tsuda, K

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Mining complex genotypic features for predicting HIV-1 drug resistance

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Saigo, H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Tsuda, K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.iscb.org/cms_addon/conferences/ismbeccb2007/
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0004-01AA-B

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.