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Gravitation field algorithm and its application in gene cluster
BACKGROUND: Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phen...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949600/ https://www.ncbi.nlm.nih.gov/pubmed/20854683 http://dx.doi.org/10.1186/1748-7188-5-32 |
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author | Zheng, Ming Liu, Gui-xia Zhou, Chun-guang Liang, Yan-chun Wang, Yan |
author_facet | Zheng, Ming Liu, Gui-xia Zhou, Chun-guang Liang, Yan-chun Wang, Yan |
author_sort | Zheng, Ming |
collection | PubMed |
description | BACKGROUND: Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. RESULTS: This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. CONCLUSIONS: The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA. |
format | Text |
id | pubmed-2949600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29496002010-11-03 Gravitation field algorithm and its application in gene cluster Zheng, Ming Liu, Gui-xia Zhou, Chun-guang Liang, Yan-chun Wang, Yan Algorithms Mol Biol Research BACKGROUND: Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. RESULTS: This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. CONCLUSIONS: The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA. BioMed Central 2010-09-20 /pmc/articles/PMC2949600/ /pubmed/20854683 http://dx.doi.org/10.1186/1748-7188-5-32 Text en Copyright ©2010 Zheng 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 Zheng, Ming Liu, Gui-xia Zhou, Chun-guang Liang, Yan-chun Wang, Yan Gravitation field algorithm and its application in gene cluster |
title | Gravitation field algorithm and its application in gene cluster |
title_full | Gravitation field algorithm and its application in gene cluster |
title_fullStr | Gravitation field algorithm and its application in gene cluster |
title_full_unstemmed | Gravitation field algorithm and its application in gene cluster |
title_short | Gravitation field algorithm and its application in gene cluster |
title_sort | gravitation field algorithm and its application in gene cluster |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949600/ https://www.ncbi.nlm.nih.gov/pubmed/20854683 http://dx.doi.org/10.1186/1748-7188-5-32 |
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