Magister Scientiae - MSc / In 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. / South Africa
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/2813 |
| Date | January 2008 |
| Creators | Duvenage, Eugene |
| Contributors | Bajic, Vladimir, Faculty of Science |
| Publisher | University of the Western Cape |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | University of the Western Cape |
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