Sandro da Silva Camargo,  Paulo Martins Engel. 

Several machine learning techniques, like neural networks, nonlinear support vector machines and decision trees, have been used to model the specificity of HIV-1 protease and to extract specific patterns from peptides cleaved by this protease. Despite many studies, no perfect rules are already known to determine the cleavage of a peptide by HIV-1 protease. These rules are useful for designing specific and efficient HIV inhibitors. Our results show that the technique of mining association rules can find several specificity rules of HIV-1 protease which presents 100% of cleavage probability. Recent papers on this subject show results in which the best rules present cleavage probability ranging from 16% to 91%.

http://www.lbd.dcc.ufmg.br/colecoes/wim/2006/012.pdf

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