Bajic, VladimirDuvenage, EugeneFaculty of Science2014-02-092024-05-172010/02/192010/02/192014-02-092024-05-172008https://hdl.handle.net/10566/15215Magister Scientiae - MScIn 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.enmiRNAGene expression regulationComputational miRNA identificationHairpin structural motifsSecondary structure calculationMachine learningGenetic algorithmRegular expressionsGenome scanHigh throughputThesisUniversity of the Western Cape