Application of Clustering and Biclustering Techniques to Yeast Metabolic Cycle

Abstract

Clustering is probably the most used techniques in the analysis of gene expression data. The goal of this technique is to find clusters of genes that have similar expression patterns. The basic assumption behind clustering approaches is that two genes with similar expression patterns are mechanically related. There are many ways in which two genes could be related (when activated by the same transcription factor, when one acts as a transcription factor for the other, when involved in the same biological process and therefore similarly regulated by the cell, etc.). This work will refer to a previously presented research paper -Yeast Metabolic Cycle – which studies genes that have similar expression patterns, we will use them to demonstrate how data mining techniques are applied to bioinformatics. A variety of tools is leveraged in order to apply Clustering and Bi clustering techniques and gains a better understanding of the biological problems we encounter in the field of systems biology.