| Download | - View accepted manuscript: Knowledge Discovery in Hepatitis C Virus Transgenic Mice (PDF, 289 KiB)
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| Author | Search for: Famili, Fazel; Search for: Ouyang, Junjun; Search for: Kryworucho, M.; Search for: Alvarez-Maya, I.; Search for: Smith, B.; Search for: Diaz-Mitoma, F. |
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| Format | Text, Article |
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| Conference | International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA-AIE 2004), May 17-20, 2004, Ottawa, Ontario, Canada |
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| Abstract | For 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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| Publication date | 2004 |
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| In | |
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| Language | English |
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| NRC number | NRCC 46545 |
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| NPARC number | 5763279 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8642e621-c86a-44ee-9ffb-c832d21f9e9a |
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| Record created | 2009-03-29 |
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| Record modified | 2021-01-05 |
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