Campus Units

Biochemistry, Biophysics and Molecular Biology, Roy J. Carver Department of, Baker Center for Bioinformatics and Biological Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

7-24-2006

Journal or Book Title

BMC Bioinformatics

Volume

7

Issue

1

First Page

355

DOI

10.1186/1471-2105-7-355

Abstract

Background

The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins.

Results

In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently.

Conclusion

Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions.

Comments

This article is published as Sen, Taner Z., Andrzej Kloczkowski, and Robert L. Jernigan. "Functional clustering of yeast proteins from the protein-protein interaction network." BMC bioinformatics 7, no. 1 (2006): 355. doi: 10.1186/1471-2105-7-355. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Copyright Owner

Sen et al

Language

en

File Format

application/pdf

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