Abstract
One of the key applications of microarray studies is to select and classify gene expression profiles of cancer and normal subjects. In this study, two hybrid approaches–genetic algorithm with decision tree (GADT) and genetic algorithm with neural network (GANN)–are utilized to select optimal gene sets which contribute to the highest classification accuracy. Two benchmark microarray datasets were tested, and the most significant disease related genes have been identified. Furthermore, the selected gene sets achieved comparably high sample classification accuracy (96.79% and 94.92% in colon cancer dataset, 98.67% and 98.05% in leukemia dataset) compared with those obtained by mRMR algorithm. The study results indicate that these two hybrid methods are able to select disease related genes and improve classification accuracy.
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Yang, P., Zhang, Z. (2007). Hybrid Methods to Select Informative Gene Sets in Microarray Data Classification. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_97
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DOI: https://doi.org/10.1007/978-3-540-76928-6_97
Publisher Name: Springer, Berlin, Heidelberg
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