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Assessment of gene order computing methods for Alzheimer's disease
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,...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552676/ https://www.ncbi.nlm.nih.gov/pubmed/23369541 http://dx.doi.org/10.1186/1755-8794-6-S1-S8 |
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author | Hu, Benqiong Jiang, Gang Pang, Chaoyang Wang, Shipeng Liu, Qingzhong Chen, Zhongxue Vanderburg, Charles R Rogers, Jack T Deng, Youping Huang, Xudong |
author_facet | Hu, Benqiong Jiang, Gang Pang, Chaoyang Wang, Shipeng Liu, Qingzhong Chen, Zhongxue Vanderburg, Charles R Rogers, Jack T Deng, Youping Huang, Xudong |
author_sort | Hu, Benqiong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3552676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35526762013-01-28 Assessment of gene order computing methods for Alzheimer's disease Hu, Benqiong Jiang, Gang Pang, Chaoyang Wang, Shipeng Liu, Qingzhong Chen, Zhongxue Vanderburg, Charles R Rogers, Jack T Deng, Youping Huang, Xudong BMC Med Genomics Research 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. BioMed Central 2013-01-23 /pmc/articles/PMC3552676/ /pubmed/23369541 http://dx.doi.org/10.1186/1755-8794-6-S1-S8 Text en Copyright ©2013 Hu et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Hu, Benqiong Jiang, Gang Pang, Chaoyang Wang, Shipeng Liu, Qingzhong Chen, Zhongxue Vanderburg, Charles R Rogers, Jack T Deng, Youping Huang, Xudong Assessment of gene order computing methods for Alzheimer's disease |
title | Assessment of gene order computing methods for Alzheimer's disease |
title_full | Assessment of gene order computing methods for Alzheimer's disease |
title_fullStr | Assessment of gene order computing methods for Alzheimer's disease |
title_full_unstemmed | Assessment of gene order computing methods for Alzheimer's disease |
title_short | Assessment of gene order computing methods for Alzheimer's disease |
title_sort | assessment of gene order computing methods for alzheimer's disease |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552676/ https://www.ncbi.nlm.nih.gov/pubmed/23369541 http://dx.doi.org/10.1186/1755-8794-6-S1-S8 |
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