FACETS: multi-faceted functional decomposition of protein interaction networks
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Seah-2012-multi-faceted functional decomposition.pdf
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1.57 MB
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Author(s)
Seah, Boon-Siew
Bhowmick, Sourav S.
Dewey, C. Forbes
Date Issued
August 2012
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Seah, B.-S., S. S. Bhowmick, and C. Forbes Dewey. “FACETS: Multi-faceted Functional Decomposition of Protein Interaction Networks.” Bioinformatics 28.20 (2012): 2624–2631.
Version
Final published version
Abstract
Motivation: 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–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity.
Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution Non-Commercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/bts469
