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  Co-Clustering of Biological Networks and Gene Expression Data

Hanisch, D., Zien, A., Zimmer, R., & Lengauer, T. (2002). Co-Clustering of Biological Networks and Gene Expression Data. Bioinformatics, (Supplement 1), 145S.

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 Creators:
Hanisch, D, Author
Zien, A1, Author              
Zimmer, R, Author
Lengauer, T, Author
Affiliations:
1External Organizations, ou_persistent22              

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 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.

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 Dates: 2002-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2146
DOI: 10.1093/bioinformatics/18.suppl_1.s145
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Title: Tenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2002)
Place of Event: Edmondon, Canada
Start-/End Date: 2002-06-29 - 2002-07-03

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: (Supplement 1) Sequence Number: - Start / End Page: 145S Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991