<p>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.</p>
| Identifer | oai:union.ndltd.org:UNWC/oai:UWC_ETD:http%3A%2F%2Fetd.uwc.ac.za%2Findex.php%3Fmodule%3Detd%26action%3Dviewtitle%26id%3Dgen8Srv25Nme4_5752_1266536340 |
| Date | January 2008 |
| Creators | Duvenage, Eugene. |
| Source Sets | Univ. of Western Cape |
| Language | English |
| Detected Language | English |
| Type | Thesis and dissertation |
| Format | |
| Coverage | ZA |
| Rights | Copyright: University of the Western Cape |
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