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.
CITATION STYLE
Famili, A. F., Ouyang, J., Kryworuchko, M., Alvarez-Maya, I., Smith, B., & Diaz-Mitoma, F. (2004). Knowledge discovery in Hepatitis C Virus transgenic mice. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3029, pp. 29–39). Springer Verlag. https://doi.org/10.1007/978-3-540-24677-0_4
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