Biclustering of gene expression data using reactive greedy randomized adaptive search procedure

<p>Abstract</p> <p>Background</p> <p>Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multi...

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Main Authors: Dharan Smitha, Nair Achuthsankar S
Format: Article
Language:English
Published: BMC 2009-01-01
Series:BMC Bioinformatics
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spelling doaj-5f8caac1434f4aa980f0345acfead7aa2020-11-25T01:02:50ZengBMCBMC Bioinformatics1471-21052009-01-0110Suppl 1S2710.1186/1471-2105-10-S1-S27Biclustering of gene expression data using reactive greedy randomized adaptive search procedureDharan SmithaNair Achuthsankar S<p>Abstract</p> <p>Background</p> <p>Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics <it>Greedy Randomized Adaptive Search Procedure (GRASP)</it>-construction and local search phases and propose a new method which is a variant of <it>GRASP </it>called <it>Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) </it>to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.</p> <p>Results</p> <p>We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach.</p> <p>Conclusion</p> <p>The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.</p>
collection DOAJ
language English
format Article
sources DOAJ
author Dharan Smitha
Nair Achuthsankar S
spellingShingle Dharan Smitha
Nair Achuthsankar S
Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
BMC Bioinformatics
author_facet Dharan Smitha
Nair Achuthsankar S
author_sort Dharan Smitha
title Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
title_short Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
title_full Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
title_fullStr Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
title_full_unstemmed Biclustering of gene expression data using reactive greedy randomized adaptive search procedure
title_sort biclustering of gene expression data using reactive greedy randomized adaptive search procedure
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2009-01-01
description <p>Abstract</p> <p>Background</p> <p>Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics <it>Greedy Randomized Adaptive Search Procedure (GRASP)</it>-construction and local search phases and propose a new method which is a variant of <it>GRASP </it>called <it>Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) </it>to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.</p> <p>Results</p> <p>We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach.</p> <p>Conclusion</p> <p>The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.</p>
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