A. Schönhuth, I. G. Costa, and A. Schliep
Accepted Japanese-German Workshop on data analysis and classification, Springer, 2006
To identify modules of interacting molecules often gene expression is analyzed with clustering methods. Constrained or semi-supervised clustering provides a framework to augment the primary, gene expression data with secondary data, to arrive at biological meaningful clusters. Here, we present an approach using constrained clustering and present favorable results on a biological data set of gene expression time-courses in Yeast together with predicted transcription factor binding site information.