Paper Info
Title
Multi-functional Protein Clustering in PPI Networks
Abstract
Protein-Protein Interaction (PPI) networks contain valuable information for the isolation of groups of proteins that participate in the same biological function. Many proteins play different roles in the cell by taking part in several processes, but isolating the different processes in which a protein is involved is often a difficult task. In this paper we present a method based on a greedy local search technique to detect functional modules in PPI graphs. The approach is conceived as a generalization of the algorithm PINCoC to generate overlapping clusters of the interaction graph in input. Due to this peculiarity, multi-facets proteins are allowed to belong to different groups corresponding to different biological processes. A comparison of the results obtained by our method with those of other well known clustering algorithms shows the capability of our approach to detect different and meaningful functional modules.
Year
DOI
Venue
2008
10.1007/978-3-540-70600-7_24
BIOINFORMATICS RESEARCH AND DEVELOPMENT, PROCEEDINGS
Keywords
Field
DocType
protein protein interaction
Cluster (physics),Graph,Pattern recognition,Computer science,Artificial intelligence,Network analysis,Local search (optimization),Bioinformatics,Cluster analysis,Functional module,Machine learning
Conference
Volume
ISSN
Citations 
13
1865-0929
6
PageRank 
References 
Authors
0.42
18
2
Name
Order
Citations
PageRank
Clara Pizzuti11250122.13
Simona E. Rombo219222.21