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A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

BACKGROUND: In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called biclustering. Biclustering algorithms are e...

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Detalles Bibliográficos
Autores principales: Ayadi, Wassim, Elloumi, Mourad, Hao, Jin-Kao
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804695/
https://www.ncbi.nlm.nih.gov/pubmed/20015398
http://dx.doi.org/10.1186/1756-0381-2-9
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author Ayadi, Wassim
Elloumi, Mourad
Hao, Jin-Kao
author_facet Ayadi, Wassim
Elloumi, Mourad
Hao, Jin-Kao
author_sort Ayadi, Wassim
collection PubMed
description BACKGROUND: In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. METHODS: We introduce BiMine, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, BiMine relies on a new evaluation function called Average Spearman's rho (ASR). Second, BiMine uses a new tree structure, called Bicluster Enumeration Tree (BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, BiMine introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters. RESULTS: The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that BiMine competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.
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spelling pubmed-28046952010-01-12 A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data Ayadi, Wassim Elloumi, Mourad Hao, Jin-Kao BioData Min Research BACKGROUND: In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. METHODS: We introduce BiMine, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, BiMine relies on a new evaluation function called Average Spearman's rho (ASR). Second, BiMine uses a new tree structure, called Bicluster Enumeration Tree (BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, BiMine introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters. RESULTS: The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that BiMine competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users. BioMed Central 2009-12-16 /pmc/articles/PMC2804695/ /pubmed/20015398 http://dx.doi.org/10.1186/1756-0381-2-9 Text en Copyright ©2009 Ayadi 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
Ayadi, Wassim
Elloumi, Mourad
Hao, Jin-Kao
A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title_full A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title_fullStr A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title_full_unstemmed A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title_short A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data
title_sort biclustering algorithm based on a bicluster enumeration tree: application to dna microarray data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804695/
https://www.ncbi.nlm.nih.gov/pubmed/20015398
http://dx.doi.org/10.1186/1756-0381-2-9
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