Abstract
A similarity measure for gene expression data should give the shapes of the patterns of the gene expression data and should be less susceptible to outliers. In this paper, we present a similarity measure for clustering gene expression time series data. Our similarity measure, PWCTM, uses the pairwise changing tendency measure of every pair of conditions. We have compared our measure with several proximity measures using k-means clustering algorithm in terms of Silhouette index, z-score and p-value. Our experimental results indicate that the gene clusters obtained with PWCTM as the similarity measure are biologically significant in the respective clusters due to their low p-values and high z-values.
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Baishya, R.C., Sarmah, R., Bhattacharyya, D.K., Dutta, M.A. (2014). A Similarity Measure for Clustering Gene Expression Data. In: Gupta, P., Zaroliagis, C. (eds) Applied Algorithms. ICAA 2014. Lecture Notes in Computer Science, vol 8321. Springer, Cham. https://doi.org/10.1007/978-3-319-04126-1_21
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DOI: https://doi.org/10.1007/978-3-319-04126-1_21
Publisher Name: Springer, Cham
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