Comparative study of gene expression data set using biclustering methods

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Author: 
Kusum Rajput, Veda, N. and Pamela Vinitha
Abstract: 

Expression technology, such as high density DNA microarray, allows us to monitor gene expression patterns at the genomic level. Advent of this technology leads to the new challenges of extracting biologically relevant knowledge from such large gene expression data sets. As a result, data mining of gene expression data has become an important area of research for biologists. Suitable mining techniques will contribute to get into the insight of the gene-gene relationships and that may further lead to discover hidden facts related to any species or microbes. To explore the gene-gene relationships through suitable data mining techniques in order to understand how genes relate and how they regulate one another. Moreover, above gene-gene relationship will be used further to extract intrinsic or embedded gene clusters which are prevalent in most of the expression of genes into clusters such that genes in the same cluster have similar gene expression patterns than genes in other clusters.

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