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Advances in Bioinformatics
Volume 2009, Article ID 787128, 9 pages
Research Article

Accurate and Scalable Techniques for the Complex/Pathway Membership Problem in Protein Networks

1Department of Computer Science, University of California, Santa Barbara, CA 93106, USA
2Department of Computer Engineering, Middle East Technical University, 06531 Ankara, Turkey

Received 1 August 2009; Accepted 2 December 2009

Academic Editor: Tamer Kahveci

Copyright © 2009 Orhan Çamoğlu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins. In this paper, we show that methods that are not affected by the degree (number of linkages) of a protein give more accurate predictions for certain complexes and pathways. We propose a network flow-based technique to compute the association probability of a pair of proteins. We extend the proposed technique using hierarchical clustering in order to scale well with the size of proteome. We also show that top-k queries are not suitable for a large number of cases, and threshold queries are more meaningful in these cases. Network flow technique with clustering is able to optimize meaningful threshold queries and answer them with high efficiency compared to a similar method that uses Monte Carlo simulation.