Assessment of gene order computing methods for Alzheimer’s disease

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dc.contributor.authorLiu, Qingzhong
dc.contributor.authorHu, Benqiong
dc.contributor.authorPang, Chaoyang
dc.contributor.authorWang, Shipend
dc.contributor.authorChen, Zhongxue
dc.contributor.authorVanderburg, Charles R
dc.contributor.authorRogers, Jack T.
dc.contributor.authorDeng, Youping
dc.contributor.authorHuang, Xudong
dc.contributor.authorJiang, Gang
dc.date.accessioned2022-01-25T16:08:03Z
dc.date.available2022-01-25T16:08:03Z
dc.date.issued2013-01-23
dc.descriptionThis article was originally published by BMC Medical Genomics in 2013. doi:10.1186/1755-8794-6-S1-S8
dc.description.abstractBackground: 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.
dc.description.sponsorshipThe work was supported by the BWH Radiology and MGH Psychiatry research funds (to X. Huang) and the Technology Innovation fund (No. 09zz028) of Key Developing Program from Education Department of Sichuan Province, China
dc.description.subjectPearson distance
dc.identifier.citationHu et al.: Assessment of gene order computing methods for Alzheimer’s disease. BMC Medical Genomics 2013 6(Suppl 1): S8. doi:10.1186/1755-8794-6-S1-S8
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3257
dc.language.isoen
dc.publisherBMC Medical Genomics
dc.subjectAlzheimer disease
dc.subjectgene clustering
dc.subjectmicroarray data
dc.subjectAnt Colony Optimization
dc.subjectGenetic Algorithm (GA)
dc.titleAssessment of gene order computing methods for Alzheimer’s disease
dc.typeArticle

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