Clustering microarray time-series data using expectation maximization and multiple profile alignment
A 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....[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009|
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