A new approach for clustering gene expression time series data
by Rosy Das, Jugal Kalita, Dhruba K. Bhattacharyya
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 5, No. 3, 2009

Abstract: Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for finding clusters in gene expression time series data using the new dissimilarity measure. A comparison with other similarity measures used for gene expression data is presented; the new dissimilarity measure is found effective. The clustering method is used in experiments that use real-life datasets and has been found to perform satisfactorily.

Online publication date: Thu, 11-Jun-2009

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