Paper Info
Title
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs
Abstract
Dense subgraphs of Protein-Protein Interaction (PPI) graphs are believed to be potential functional modules and play an important role in inferring the functional behavior of proteins. PPI graphs are known to exhibit the scale-free property in which a few nodes (hubs) are highly connected. This scale-free topology of PPI graphs makes it hard to isolate dense subgraphs effectively. In this paper, we propose a novel refinement method based on neighborhoods and the biological importance of hub proteins. We show that this refinement improves the functional modularity of the PPI graph and leads to effective clustering into dense components. A detailed comparison of these dense components with the ones obtained from the original PPI graph reveal three major benefits of the refinement: i) Enhancement of existing functional groupings; ii) Isolation of new functional groupings; and iii) Soft clustering of multifunctional hub proteins to multiple functional groupings.
Year
DOI
Venue
2006
10.1007/11871637_36
PKDD
Keywords
Field
DocType
dense component,functional grouping,protein-protein interactions graph,functional modularity,potential functional module,original ppi graph,new functional grouping,functional behavior,multiple functional grouping,dense subgraphs,improving functional modularity,ppi graph,hub-induced subgraphs,protein protein interaction,scale free
Data mining,Graph,Fuzzy clustering,Combinatorics,Protein–protein interaction,Computer science,Theoretical computer science,Knowledge extraction,Cluster analysis,Functional module,Modularity
Conference
Volume
ISSN
ISBN
4213
0302-9743
3-540-45374-1
Citations 
PageRank 
References 
25
1.18
6
Authors
4
Name
Order
Citations
PageRank
Duygu Ucar134719.69
Sitaram Asur2136864.36
umit v catalyurek31405101.09
Srinivasan Parthasarathy44666375.76