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A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

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dc.contributor.author Alizadeh Noori, Farhad
dc.date.accessioned 2012-09-18T13:38:36Z
dc.date.available 2012-09-18T13:38:36Z
dc.date.issued 2012-09-18
dc.identifier.uri http://hdl.handle.net/10464/4101
dc.description.abstract Understanding 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.iso eng en_US
dc.publisher Brock University en_US
dc.subject Bioinformatics en_US
dc.subject Motif Discovery en_US
dc.subject Side Effect Machines en_US
dc.subject Evolutionary Computation en_US
dc.title A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name M.Sc. Computer Science en_US
dc.degree.level Masters en_US
dc.contributor.department Department of Computer Science en_US
dc.degree.discipline Faculty of Mathematics and Science en_US


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