Clustering Functionally Similar Genes from Microarray Data
Microarray technology is one of the important biotechnological means that has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes (BP) and across collections of related samples [1–3]. An important application of microarray data is to elucidate the patterns hidden in gene expression data for an enhanced understanding of functional genomics.
A microarray gene expression data set can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample or time point, and each entry of the matrix is the measured expression level of a particular gene in a sample or time point, respectively [1–3]. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the pattern recognition process to reveal natural structures and identify interesting patterns in the underlying data .
Cluster analysis is a technique to find natural groups present in the gene set. It divides a given gene set into a set of clusters in such a way that two genes from the same cluster are as similar as possible and the genes from different clusters are ...