Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USA.
Adv Exp Med Biol. 2010;680:199-204. doi: 10.1007/978-1-4419-5913-3_23.
In this paper, we propose two new approaches, FM-GA and CM-GA, to identify significant genes from microarray datasets. FM-GA and CM-GA combine our innovative FM-test and CM-test with genetic algorithm (GA), respectively, and leverage the strengths of GA. The performance of FM-GA and CM-GA was evaluated by the classification accuracy of decision trees constructed with the selected genes. Experiments were conducted to demonstrate the superiority of the proposed method over other approaches.
在本文中，我们提出了两种新的方法，FM-GA 和 CM-GA，用于从微阵列数据集识别显著基因。FM-GA 和 CM-GA 分别将我们的创新 FM 检验和 CM 检验与遗传算法 (GA) 相结合，并利用 GA 的优势。FM-GA 和 CM-GA 的性能通过使用所选基因构建的决策树的分类准确性来评估。实验证明了所提出的方法优于其他方法。
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