Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm

• Published in: International journal of data mining and bioinformatics Vol. 6; no. 6; p. 617
• Main Authors: Pang, Chaoyang, Jiang, Gang, Wang, Shipeng, Hu, Benqiong, Liu, Qingzhong, Deng, Youping, Huang, Xudong
• Format: Journal Article
• Language: English
• Published: Switzerland 2012
• Subjects: Algorithms; Alzheimer Disease - genetics; Cluster Analysis; Gene Expression; Gene Expression Profiling - methods; Gene Order; Humans; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated - methods
• ISSN: 1748-5673
• DOI: 10.1504/IJDMB.2012.050247

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: a different distance formula generated a different quality of gene order, the squared Euclidean distance approach produced the optimal AD-related gene order.