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dc.contributor.advisorBajic, Vladimir
dc.contributor.authorDuvenage, Eugene
dc.contributor.other
dc.contributor.otherFaculty of Science
dc.date.accessioned2014-02-09T01:20:55Z
dc.date.available2010/02/19 01:39
dc.date.available2010/02/19
dc.date.available2014-02-09T01:20:55Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/11394/2813
dc.descriptionMagister Scientiae - MScen_US
dc.description.abstractIn summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.en_US
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.subjectmiRNAen_US
dc.subjectGene expression regulationen_US
dc.subjectComputational miRNA identificationen_US
dc.subjectHairpin structural motifsen_US
dc.subjectSecondary structure calculationen_US
dc.subjectMachine learningen_US
dc.subjectGenetic algorithmen_US
dc.subjectRegular expressionsen_US
dc.subjectGenome scanen_US
dc.subjectHigh throughputen_US
dc.titlemiRNAMatcher: High throughput miRNA discovery using regular expressions obtained via a genetic algorithmen_US
dc.typeThesisen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.description.countrySouth Africa


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