Functional clustering of yeast proteins from the protein-protein interaction network.
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.


CONCLUSIONS
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.
Sen
Taner Z
TZ
L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University Ames, IA 50011, USA. taner@iastate.edu
Kloczkowski
Andrzej
A
Jernigan
Robert L
RL
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins