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Applying Machine Learning Methods to Suggest Network Involvement and Functionality of Genes in Saccharomyces cerevisiae
dc.contributor.advisor | Agah, Arvin | |
dc.contributor.advisor | Tsatsoulis, Costas | |
dc.contributor.author | Amthauer, Heather A. | |
dc.date.accessioned | 2008-12-01T02:18:29Z | |
dc.date.available | 2008-12-01T02:18:29Z | |
dc.date.issued | 2008-10-10 | |
dc.date.submitted | 2008 | |
dc.identifier.other | http://dissertations2.umi.com/ku:2740 | |
dc.identifier.uri | http://hdl.handle.net/1808/4284 | |
dc.description.abstract | Elucidating genetic networks provides the foundation for the development of new treatments or cures for diseased pathways, and determining novel gene functionality is critical for bringing a better understanding on how an organism functions as a whole. In this dissertation, I developed a methodology that correctly locates genes that may be involved in genetic networks with a given gene based on its location over 50% of the time or based on its description over 43% of the time. I also developed a methodology that makes it easier to predict how a gene product behaves in a cellular context by suggesting the correct Gene Ontology term over 80% of the time. The designed software provides researchers with a way to focus their search for coregulated genes which will lead to better microarray chip design and limits the list of possible functions of a gene product. This ultimately saves the researcher time and money. | |
dc.format.extent | 1452 pages | |
dc.language.iso | EN | |
dc.publisher | University of Kansas | |
dc.rights | This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author. | |
dc.subject | Computer science | |
dc.subject | Biology | |
dc.subject | Bioinformatics | |
dc.subject | Machine learning | |
dc.subject | Data mining | |
dc.subject | Saccharomyces cerevisiae | |
dc.title | Applying Machine Learning Methods to Suggest Network Involvement and Functionality of Genes in Saccharomyces cerevisiae | |
dc.type | Dissertation | |
dc.contributor.cmtemember | Alexander, Perry | |
dc.contributor.cmtemember | Chen, Xue-wen | |
dc.contributor.cmtemember | Ercal-Ozkaya, Gunes | |
dc.contributor.cmtemember | Kelly, John | |
dc.thesis.degreeDiscipline | Electrical Engineering & Computer Science | |
dc.thesis.degreeLevel | PH.D. | |
kusw.oastatus | na | |
kusw.oapolicy | This item does not meet KU Open Access policy criteria. | |
kusw.bibid | 6857242 | |
dc.rights.accessrights | openAccess |
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Dissertations [4889]
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Engineering Dissertations and Theses [1055]