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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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We propose a simple statistical 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. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-15-309","type":"journal-article","created":{"date-parts":[[2014,9,19]],"date-time":"2014-09-19T13:01:03Z","timestamp":1411131663000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting virus mutations through statistical relational learning"],"prefix":"10.1186","volume":"15","author":[{"given":"Elisa","family":"Cilia","sequence":"first","affiliation":[]},{"given":"Stefano","family":"Teso","sequence":"additional","affiliation":[]},{"given":"Sergio","family":"Ammendola","sequence":"additional","affiliation":[]},{"given":"Tom","family":"Lenaerts","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Passerini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,9,19]]},"reference":[{"issue":"2","key":"6635_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1006\/abbi.1999.1209","volume":"365","author":"M G\u00f6tte","year":"1999","unstructured":"G\u00f6tte M, Li X, Wainberg M: HIV-1 reverse transcription: a brief overview focused on structure-function relationships among molecules involved in initiation of the reaction. 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