Knowledge Discovery in Hepatitis C Virus Transgenic Mice

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ConferenceAccepted for Presentation at the International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA-AIE 2004), May 17-20, 2004., Ottawa, Ontario, Canada
AbstractFor the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for useful patterns in the data. The approach involves validation and elimination of irrelevant data, extensive data pre-processing, data visualization, exploratory clustering, pattern recognition and model summarization. We have evaluated our method using data from microarray experiments in a Hepatitis C Virus transgenic mouse model. We demonstrate that from a total of 15311 genes (attributes) we can generate simple models and identify a small number of genes that can be used for future classifications. The approach has potential for future disease classification, diagnostic and virology applications.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number46545
NPARC number5763279
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Record identifier8642e621-c86a-44ee-9ffb-c832d21f9e9a
Record created2009-03-29
Record modified2016-05-09
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