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Applying Machine Learning Methods to Suggest Network Involvement and Functionality of Genes in Saccharomyces cerevisiae
Amthauer, Heather A.
Amthauer, Heather A.
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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.
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Date
2008-10-10
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University of Kansas
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umi-ku-2740_1.pdf
Adobe PDF, 12.46 MB
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Keywords
Computer science, Biology, Bioinformatics, Machine learning, Data mining, Saccharomyces cerevisiae