Abstract
Genes with similar expression profiles are expected to be functionally related or co-regulated. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a k-means clustering approach based on a multiple alignment of natural cubic spline representations of gene expression profiles. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a time-interval, defined on a set of profiles. Preliminary experiments on a well-known data set of 221 pre-clustered Saccharomyces cerevisiae gene expression profiles yields excellent results with 79.64% accuracy.
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Subhani, N., Ngom, A., Rueda, L., Burden, C. (2009). Microarray Time-Series Data Clustering via Multiple Alignment of Gene Expression Profiles. In: Kadirkamanathan, V., Sanguinetti, G., Girolami, M., Niranjan, M., Noirel, J. (eds) Pattern Recognition in Bioinformatics. PRIB 2009. Lecture Notes in Computer Science(), vol 5780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04031-3_33
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DOI: https://doi.org/10.1007/978-3-642-04031-3_33
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