Paper Info
Title
Clustering proteins from interaction networks for the prediction of cellular functions.
Abstract
Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes.Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins.We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.
Year
DOI
Venue
2004
10.1186/1471-2105-5-95
BMC Bioinformatics
Keywords
Field
DocType
bioinformatics,computational biology,high throughput,microarrays,genetics,interaction network,predictive value of tests,computer graphics,cluster analysis,algorithms
Disjoint sets,Vertex (geometry),Computer science,Theoretical computer science,Interaction network,Saccharomyces cerevisiae Proteins,Bioinformatics,Cluster analysis,Computer graphics,Probability density function,Test data generation
Journal
Volume
Issue
ISSN
5
1
1471-2105
Citations 
PageRank 
References 
59
5.32
2
Authors
3
Name
Order
Citations
PageRank
Christine Brun116911.90
Carl Herrmann212910.38
A. Guénoche322941.64