Subhani, NumanulRueda, LuisNgom, AliouneBurden, Conrad2015-12-132015-12-13November 19781424451210http://hdl.handle.net/1885/81758A common problem in biology is to partition a set of experimental data into clusters in such a way that the data points within the same cluster are highly similar while data points in different clusters are very different. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a EM 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 yield encouraging results with 83.26% accuracy.Keywords: Common problems; Cubic spline; Data points; Data sets; EM clustering; Expectation Maximization; Experimental data; Gene expression profiles; Multiple alignment; Pairwise alignment; Piecewise linear; Profile alignment; Saccharomyces cerevisiae; Squared err Clustering; Cubic spline; Gene expression profiles; Microarrays; Profile alignment; Time-series data200910.1109/BIBMW.2009.53321282016-02-24