Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm.
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.
Group of Gene Computation, College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610066, China cypang@live.cn
Chaoyang
C
Pang
Gang
G
Jiang
Shipeng
S
Wang
Benqiong
B
Hu
Qingzhong
Q
Liu
Youping
Y
Deng
Xudong
X
Huang
D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics*; D016000:Cluster Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D023061:Gene Order; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods