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dc.contributor.authorBrown, Joseph Alexanderen_US
dc.date.accessioned2010-03-09T20:22:58Z
dc.date.available2010-03-09T20:22:58Z
dc.date.issued2010-03-09T20:22:58Z
dc.identifier.urihttp://hdl.handle.net/10464/2941
dc.description.abstractBioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectComputer algorithms.en_US
dc.subjectBioinformaticsen_US
dc.titleDecoding algorithms using side-effect machinesen_US
dc.typeElectronic Thesis or Dissertationen
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-07T02:24:38Z


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