Enhanced Biclustering for Gene Expression Data

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 coregulation 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.