Knowledge Discovery in Hepatitis C Virus Transgenic Mice

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TypeArticle
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.
Publication date
LanguageEnglish
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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