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This article is part of the supplement: ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011

Open Access Proceedings

FUSE: a profit maximization approach for functional summarization of biological networks

Boon-Siew Seah1*, Sourav S Bhowmick1, C Forbes Dewey2 and Hanry Yu3

Author Affiliations

1 School of Computer Engineering, Nanyang Technological University, Singapore

2 Department of Biological Engineering, Massachusetts Institute of Technology, USA

3 Department of Physiology, National University of Singapore, Singapore

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BMC Bioinformatics 2012, 13(Suppl 3):S10  doi:10.1186/1471-2105-13-S3-S10

Published: 21 March 2012

Abstract

Background

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint.

Results

We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI.

Conclusion

By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.