INVESTIGATION OF DIFFERENTIALLY EXPRESSED GENE RELATED TO HUNTINGTON'S DISEASE USING GENETIC ALGORITHM

Maha S. Mohamed, W. Al-Atabany, V. F. Ghoneim
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Abstract

neurodegenerative diseases have complex pathological mechanisms. Detecting disease-associated genes with typical differentially expressed gene selection approaches are ineffective. Recent studies have shown that wrappers Evolutionary optimization methods perform well in feature selection for high dimensional data, but they are computationally costly. This paper proposes a simple method based on a genetic algorithm engaged with the Empirical Bays T-statistics test to enhance the disease-associated gene selection process. The proposed method is applied to Affymetrix microarray data from Huntington's disease. 40 disease-associated genes are discovered as biomarkers. Moreover, the proposed approach improved the disease-associated gene prediction process. The classification accuracy for selected genes is calculated using the K nearest neighbor with leave-one-out cross-validation. The accuracy ranges from 93.1 to 100 for 3 different brain regions, suggesting the effectiveness and robustness of selected genes.
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应用遗传算法研究亨廷顿氏病相关差异表达基因
神经退行性疾病具有复杂的病理机制。用典型的差异表达基因选择方法检测疾病相关基因是无效的。近年来的研究表明,包装器进化优化方法在高维数据特征选择方面表现良好,但计算量较大。本文提出了一种基于遗传算法的简单方法,结合Empirical Bays t统计检验来增强疾病相关基因选择过程。该方法已应用于亨廷顿氏病的Affymetrix微阵列数据。发现40种疾病相关基因作为生物标志物。此外,该方法改进了疾病相关基因的预测过程。所选基因的分类精度使用K近邻和留一交叉验证来计算。对于3个不同的大脑区域,准确率在93.1到100之间,表明所选基因的有效性和稳健性。
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