Gene order computation using Alzheimer's DNA microarray gene expression data and the ant colony optimisation algorithm Online publication date: Wed, 17-Dec-2014
by Chaoyang Pang; Gang Jiang; Shipeng Wang; Benqiong Hu; Qingzhong Liu; Youping Deng; Xudong Huang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 6, No. 6, 2012
Abstract: As Alzheimer's Disease (AD) is the most common form of dementia, the study of AD-related genes via biocomputation is an important research topic. One method of studying AD-related gene is to cluster similar genes together into a gene order. Gene order is a good clustering method as the results can be optimal globally while other clustering methods are only optimal locally. Herein we use the Ant Colony Optimisation (ACO)-based algorithm to calculate the gene order from an Alzheimer's DNA microarray dataset. We test it with four distance measurements: Pearson distance, Spearmen distance, Euclidean distance, and squared Euclidean distance. Our computing results indicate: 1) a different distance formula generated a different quality of gene order; 2) the squared Euclidean distance approach produced the optimal AD-related gene order.
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