@article {bioinflmu-326,
	title = {{Co-clustering of biological networks and gene expression data}},
	journal = {Bioinformatics},
	volume = {18},
	number = {90001},
	year = {2002},
	pages = {S145-S154 },
	abstract = {Motivation: Large scale gene expression data are often analysed by

clustering genes based on gene expression data alone, though a priori knowledge

in the form of biological networks is available. The use of this additional

information promises to improve exploratory analysis considerably. Results: We

propose constructing a distance function which combines information from

expression data and biological networks. Based on this function, we compute a

joint clustering of genes and vertices of the network. This general approach is

elaborated for metabolic networks. We define a graph distance function on such

networks and combine it with a correlation-based distance function for gene

expression measurements. A hierarchical clustering and an associated statistical

measure is computed to arrive at a reasonable number of clusters. Our method is

validated using expression data of the yeast diauxic shift. The resulting

clusters are easily interpretable in terms of the biochemical network and the

gene expression data and suggest that our method is able to automatically

identify processes that are relevant under the measured conditions. Contact:

Daniel.Hanisch@scai.fhg.de Keywords: gene expression; biological networks;

metabolic networks; co-clustering; clustering.},
	keywords = {expressionlab@lmu},
	doi = {10.1093/bioinformatics/18.suppl_1.S145},
	pdf = {<a href="/files/oldwebfm//Publications/Co-clustering_of_biological_networks_and_gene_expression_data._Bioinformatics.pdf">PDF</a>},
	author = {Daniel Hanisch and Alexander Zien and Ralf Zimmer and Thomas Lengauer}
}