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.
Online publication date: Wed, 17-Dec-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com