title: Predicting virus mutations through statistical relational learning.
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 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.
  
  
  CONCLUSIONS
  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.
authors.forename: Elisa
authors.initials: E
authors.lastname: Cilia
authors.forename: Stefano
authors.initials: S
authors.lastname: Teso
authors.forename: Sergio
authors.initials: S
authors.lastname: Ammendola
authors.forename: Tom
authors.initials: T
authors.lastname: Lenaerts
authors.affiliation: Department of Computer Science and Information Engineering, University of Trento, via Sommarive 5, I-38123 (Povo) Trento, Italy. passerini@disi.unitn.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Passerini
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D024882:Drug Resistance, Viral; D006678:HIV / Q000187:drug effects / Q000201:enzymology / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology*; D054303:HIV Reverse Transcriptase / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D009154:Mutation; D009705:Nucleosides / Q000737:chemistry / Q000494:pharmacology; D018894:Reverse Transcriptase Inhibitors / Q000737:chemistry / Q000494:pharmacology*
chemical_list: D009705:Nucleosides; D018894:Reverse Transcriptase Inhibitors; D054303:HIV Reverse Transcriptase