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Predicting virus mutations through statistical relational learning

TitlePredicting virus mutations through statistical relational learning
Publication TypeJournal Article
Year of Publication2014
AuthorsElisa, C, Teso, S, Ammendola, S, Lenaerts, T, Passerini, A
JournalBMC bioinformatics

BACKGROUND: Viruses are typically characterized by high mutation rates, which allow them to quickly developdrug-resistant mutations. Mining relevant rules from mutation data can be extremely useful tounderstand the virus adaptation mechanism and to design drugs that effectively counter potentiallyresistant mutants. RESULTS: We propose a simple statistical relational learning approach for mutant prediction where the inputconsists of mutation data with drug-resistance information, either as sets of mutations conferringresistance 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 generatea set of potentially resistant mutants. Learning a weighted combination of rules allows to attachgenerated mutants with a resistance score as predicted by the statistical relational model and selectonly the highest scoring ones. CONCLUSIONS: Promising results were obtained in generating resistant mutations for both nucleoside andnon-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily tolearning mutants characterized by more complex rules correlating multiple mutations.


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