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Assessment of gene order computing methods for Alzheimer's disease

机译：阿尔茨海默氏病基因顺序计算方法的评估

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Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.
机译：背景老年痴呆症最常见的形式是阿尔茨海默病（AD）的计算基因组学，是AD研究的新兴领域。通过计算基因顺序，该领域包括AD基因聚类，与大多数其他聚类方法相比，该基因顺序可产生更高质量的基因聚类模式。但是，很少有可用的基因顺序计算方法，例如遗传算法（GA）和蚁群优化（ACO）。此外，还不知道它们在使用AD微阵列数据进行基因顺序计算中的性能。因此，我们着手评估具有不同距离公式的当前基因顺序计算方法的性能，并确定与基因顺序计算相关的其他功能。方法使用不同的距离公式-皮尔逊距离和欧几里德距离，平方欧几里德距离和其他条件，分别通过ACO和GA（包括标准GA和改进GA）方法计算基因顺序。比较了基因顺序的质量，并从计算的基因顺序中识别出新特征。结果与本研究中测试的GA方法相比，ACO在计算基因顺序时最适合AD微阵列数据。此外，还揭示了以下特征：不同的距离公式产生了不同的基因顺序质量，并且当与GA和ACO方法同时用于AD微阵列数据时，常用的Pearson距离不是最佳的距离公式。结论与Pearson距离和Euclidean距离相比，平方的Euclidean距离产生了用GA和ACO方法计算出的最佳质量基因顺序。

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