Predicting HIV drug resistance with neural networks | Zendy

Sorin Drăghici | Zendy; R. Brian Potter | Zendy

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Predicting HIV drug resistance with neural networks

Author(s) -

Sorin Drăghici,

R. Brian Potter

Publication year - 2002

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/19.1.98

Subject(s) - hiv 1 protease , artificial intelligence , classifier (uml) , saquinavir , protease , machine learning , computer science , artificial neural network , indinavir , human immunodeficiency virus (hiv) , drug resistance , computational biology , pattern recognition (psychology) , biology , virology , antiretroviral therapy , viral load , genetics , biochemistry , enzyme

Drug resistance is a very important factor influencing the failure of current HIV therapies. The ability to predict the drug resistance of HIV protease mutants may be useful in developing more effective and longer lasting treatment regimens.

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