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Predicting virus mutations through relational learning

TitlePredicting virus mutations through relational learning
Publication TypeConference Paper
Year of Publication2012
AuthorsCilia, E, Teso, S, Ammendola, S, Lenaerts, T, Andrea, P
Conference NameAnnotation, Interpretation and Management of Mutations (AIMM) Workshop@ECCB2012
Date PublishedSeptember
Conference LocationBasil, Switzerland
Abstract

Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants.

Results: We propose a simple relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants.

Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and nonnucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

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