Predicting virus mutations through relational learning

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 use 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...