Summary
For more than two decades AIDS remains a terminal disease and no efficient therapy exists. The high mutability of HIV leads to serious problems in designing efficient anti-viral drugs. Soon after introducing a new drug, there appear HIV strains that are resistant to the applied agent. In order to help overcoming resistance, we constructed a classificatory model of genotype-resistance relationship. To derive our model, we use rough sets theory. Furthermore, by incorporating existing biochemical knowledge into our model, it gains biological meaning and becomes helpful in understanding drug resistance phenomenon. Our highly accurate classifiers are based on a number of explicit, easy-to-interpret IF-THEN rules. For every position in amino acid sequence of viral enzyme reverse transcriptase (one of two main targets for anti-viral drugs), the rules describe the way the biochemical properties of amino acid have to change in order to acquire drug resistance. Preliminary biomolecular analysis suggests the applicability of the model.
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Kierczak, M., Rudnicki, W.R., Komorowski, J. (2008). Construction of Rough Set-Based Classifiers for Predicting HIV Resistance to Nucleoside Reverse Transcriptase Inhibitors. In: Bello, R., Falcón, R., Pedrycz, W., Kacprzyk, J. (eds) Granular Computing: At the Junction of Rough Sets and Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76973-6_16
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DOI: https://doi.org/10.1007/978-3-540-76973-6_16
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