Enhanced Biclustering for Gene Expression Data
R. Parimala

R. Parimala, Assistant Professor, Department of CSE, Info Institute of Engineering. Coimbatore, Tamilnadu, India.
Manuscript received on April 05, 2013. | Revised Manuscript received on April 11, 2013. | Manuscript published on April 15, 2013. | PP: 11-14 | Volume-1 Issue-5, April 2013. | Retrieval Number: E0204041513/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Microarray technology is a powerful method for monitoring the expression level of thousands of genes in parallel. Using this technology, the expression levels of genes are measured. Microarray data is represented in N × M matrix. Each row indicates genes and each column indicates condition. In Gene Expression data, standard clustering algorithms are called as global clustering. In global clustering, genes are analyzed under all experimental conditions based on their expression. Biclustering is a very popular method to identify hidden co-regulation patterns among genes and to identify the local structures of genes and conditions. In existing system, Cheng and Church biclustering algorithm is presented as an alternative approach to standard clustering techniques to identify local structures and also identify subsets of genes that shows similar expression patterns across specific subsets of experimental conditions and vice versa. Clustering the microarray data is based on user defined threshold value, this affects the quality of biclusters formed. In proposed scheme, threshold value ∂ is calculated rather than user defined threshold. Biclusters are formed based on the low mean squared residues and ∂, which would improve the quality of the biclusters.
Keywords: Microarray technology, Clustering, Biclustering, gene expression data