Abstract
We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We also introduce a variant of a clustering index that can effectively decide upon the optimal number of clusters for a given dataset. The clustering method is based on a profile-alignment approach, which minimizes the mean-square-error of the first order differentials, to hierarchically cluster microarray time-series data. The effectiveness of our algorithm has been tested on datasets drawn from standard experiments, showing that our approach can effectively cluster the datasets based on profile similarity.
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Bari, A., Rueda, L. (2006). A New Profile Alignment Method for Clustering Gene Expression Data. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_8
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DOI: https://doi.org/10.1007/11766247_8
Publisher Name: Springer, Berlin, Heidelberg
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