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Biclustering of Gene Expression Data using a Two - Phase Method

by Madhuleena Das, Bhogeswar Borah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 13
Year of Publication: 2014
Authors: Madhuleena Das, Bhogeswar Borah
10.5120/18132-9232

Madhuleena Das, Bhogeswar Borah . Biclustering of Gene Expression Data using a Two - Phase Method. International Journal of Computer Applications. 103, 13 ( October 2014), 6-10. DOI=10.5120/18132-9232

@article{ 10.5120/18132-9232,
author = { Madhuleena Das, Bhogeswar Borah },
title = { Biclustering of Gene Expression Data using a Two - Phase Method },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 13 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number13/18132-9232/ },
doi = { 10.5120/18132-9232 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:26.458923+05:30
%A Madhuleena Das
%A Bhogeswar Borah
%T Biclustering of Gene Expression Data using a Two - Phase Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 13
%P 6-10
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biclustering is a very useful data mining technique which identifies coherent patterns from microarray gene expression data. A bicluster of a gene expression dataset is a subset of genes which exhibit similar expression patterns along a subset of conditions. Biclustering is a powerful analytical tool for the biologist and has generated considerable interest over the past few decades. Many biclustering algorithms optimize a mean squared residue to discover biclusters from a gene expression dataset. In this paper a Two-Phase method of finding a bicluster is developed. In the first phase, a modified version of k-means algorithm is applied to the gene expression data to generate k clusters. In the second phase, an iterative search is performed to check the possibility of removing more genes and conditions within the given threshold value of mean squared residue score. Experimental results on yeast dataset show that our approach can effectively find high quality biclusters

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gene expression data data mining clustering biclustering.

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