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Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data

by Shyama Das, Sumam Mary Idicula
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 3
Year of Publication: 2010
Authors: Shyama Das, Sumam Mary Idicula
10.5120/650-907

Shyama Das, Sumam Mary Idicula . Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data. International Journal of Computer Applications. 2, 3 ( May 2010), 6-13. DOI=10.5120/650-907

@article{ 10.5120/650-907,
author = { Shyama Das, Sumam Mary Idicula },
title = { Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data },
journal = { International Journal of Computer Applications },
issue_date = { May 2010 },
volume = { 2 },
number = { 3 },
month = { May },
year = { 2010 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume2/number3/650-907/ },
doi = { 10.5120/650-907 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:50:38.869827+05:30
%A Shyama Das
%A Sumam Mary Idicula
%T Application of Greedy Randomized Adaptive Search Procedure to the Biclustering of Gene Expression Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 2
%N 3
%P 6-13
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Microarray technology demands the development of data mining algorithms for extracting useful and novel patterns. A bicluster of a gene expression dataset is a local pattern such that the genes in the bicluster exhibit similar expression patterns through a subset of conditions. In this study biclusters are detected in two steps. In the first step high quality bicluster seeds are generated using K-Means clustering algorithm. These seeds are then enlarged using a multistart metaheuristic method Greedy Randomized Adaptive Search Procedure (GRASP).In GRASP there are two phases, construction and local search. The Experimental results on the benchmark datasets demonstrate that GRASP is capable of identifying high qua;ity biclusters compared to many of the already existing biclustering algorithms. Moreover far better biclusters are obtained in this algorithm compared to the already existing algorithms based on the same GRASP metaheuristics. In this study GRASP is applied for the first time to identify biclusters from Human Lymphoma dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

Gene expression data Greedy randomized adaptive search procedure K-Means clustering Biclustering Data mining

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