Title: Gene order computation using Alzheimer's DNA microarray gene expression data and the ant colony optimisation algorithm
Authors: Chaoyang Pang; Gang Jiang; Shipeng Wang; Benqiong Hu; Qingzhong Liu; Youping Deng; Xudong Huang
Addresses: Group of Gene Computation, College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610066, China ' Group of Gene Computation, College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610066, China ' Group of Gene Computation, College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610066, China ' College of Information Management, Chengdu University of Technology, Chengdu 610059, China ' Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA ' Rush University Cancer Center, Rush University Medical Center, Chicago, IL 60612, USA ' Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
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.
Keywords: gene order; ACO; ant colony optimisation; Alzheimer's disease; DNA microarrays; gene expression data; dementia; clustering; squared Euclidean distance.
International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.6, pp.617 - 632
Received: 22 Jul 2010
Accepted: 09 Dec 2010
Published online: 13 Nov 2012 *