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Many methods have been proposed to identify informative subsets of genes in microarray studies in order to focus the research. For instance, the recently proposed binarization of consensus partition matrices (Bi-CoPaM) method has, amongst its various features, the ability to generate tight clusters of genes while leaving many genes unassigned from all clusters. We propose exploiting this particular feature by applying the Bi-CoPaM over genome-wide microarray data from multiple datasets to generate more clusters than required. Then, these clusters are tightened so that most of their genes are left unassigned from all clusters, and most of the clusters are left totally empty. The tightened clusters, which are still not empty, include those genes that are consistently co-expressed in multiple datasets when examined by various clustering methods. An example of this is demonstrated in this paper for cyclic and acyclic genes as well as for genes that are highly expressed and that are not. Thus, the results of our proposed approach cannot be reproduced by other methods of genes' periodicity identification or by other methods of clustering. © 2013 IEEE.

Original publication




Conference paper

Publication Date



1172 - 1176