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dc.contributor.authorAlizadeh Noori, Farhad
dc.date.accessioned2012-09-18T13:38:36Z
dc.date.available2012-09-18T13:38:36Z
dc.date.issued2012-09-18
dc.identifier.urihttp://hdl.handle.net/10464/4101
dc.description.abstractUnderstanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectBioinformaticsen_US
dc.subjectMotif Discoveryen_US
dc.subjectSide Effect Machinesen_US
dc.subjectEvolutionary Computationen_US
dc.titleA Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discoveryen_US
dc.typeElectronic Thesis or Dissertationen_US
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


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