Semi-supervised Clustering of Yeast Gene Expression

A. Sch√∂nhuth, I.G. Costa and A. Schliep

In Cooperation in Classification and Data Analysis, Springer, 151–160, 2009. Proceedings of Two German-Japanese Workshops .

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.

A reprint is available as PDF.

DOI: 10.1007/978-3-642-00668-5_16.

The publication includes results from the following projects or software tools: GQL, GenExpTimecourses, GHMM.

Further publications by Alexander Schliep, Ivan G Costa.