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This article is part of the supplement: Proceedings of the 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP'11)

Open Access Research

Assessment of gene order computing methods for Alzheimer's disease

Benqiong Hu1, Gang Jiang2, Chaoyang Pang2, Shipeng Wang2, Qingzhong Liu3, Zhongxue Chen4, Charles R Vanderburg5, Jack T Rogers6, Youping Deng7 and Xudong Huang6*

Author Affiliations

1 College of Management Science, Chengdu University of Technology, Chengdu 610059, China

2 Group of Gene Computation, College of Mathematics and Software Science, Sichuan Normal University, Chengdu 610066, China

3 Department of Computer Science, Sam Houston State University, Huntsville, TX 7734, USA

4 Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, 1025 E. 7th Street, Bloomington, IN 47405-7109, USA

5 Harvard NeuroDiscovery Center and Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA

6 Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA

7 Cancer Bioinformatics, Rush University Cancer Center, and Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA

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BMC Medical Genomics 2013, 6(Suppl 1):S8  doi:10.1186/1755-8794-6-S1-S8

Published: 23 January 2013

Abstract

Background

Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation.

Methods

Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified.

Results

Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data.

Conclusion

Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.