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Biclustering algorithms are an important tool for the analysis of gene expression data. Research on analysis of gene expression data includes identification of groups of genes with similar expression patterns. Standard clustering methods for the analysis of gene expression data only identify the global similarity while missing the local patterns of expression similarity, i.e. genes could behave similar over only a subset of the observations. In order to identify such patterns biclustering approaches have been introduced. This paper compares the performance of five important biclustering methods by applying them on a gene expression dataset of yeast. The dataset of Saccharomyces cerevisiae comprised of 5736 genes expressed in 112 strains of yeast. The performance is scored based on the algorithm's ability to find functionally enriched as well as transcription factor target site enriched groups of genes. Our studies shows that among the chosen five biclustering algorithms SAMBA and ISA showed the best performance on the basis of functional enrichment. Biclusters were also obtained through remaining three algorithms also but they were not functionally enriched.
Date of Conference: 14-15 Dec. 2009