Comparative analysis of clustering methods for gene expression time course data

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Abstract
This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series). Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.