Title: A new approach for clustering gene expression time series data

Authors: Rosy Das, Jugal Kalita, Dhruba K. Bhattacharyya

Addresses: Department of Computer Science and Engineering, Tezpur University, Napaam 784028, Assam, India. ' Department of Computer Science, University of Colorado, Colorado Springs CO 80918, USA. ' Department of Computer Science and Engineering, Tezpur University, Napaam 784028, Assam, India

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.

Keywords: gene expression; microarrays; coherent patterns; grid-based clustering; graph-based clustering; proximity measure; bioinformatics; time series data; dissimilarity measures.

DOI: 10.1504/IJBRA.2009.026422

International Journal of Bioinformatics Research and Applications, 2009 Vol.5 No.3, pp.310 - 328

Published online: 11 Jun 2009 *

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