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Bioinformatics 2008 24(13):i250-i268; doi:10.1093/bioinformatics/btn164
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Protein complex identification by supervised graph local clustering

Yanjun Qi 1, Fernanda Balem 2, Christos Faloutsos 1, Judith Klein-Seetharaman 1,2 and Ziv Bar-Joseph 1,*

1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 and 2Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphs representing protein complexes. Previous methods for performing such search relied on the assumption that complexes form a clique in that graph. While this assumption is true for some complexes, it does not hold for many others. New algorithms are required in order to recover complexes with other types of topological structure.

Results: We present an algorithm for inferring protein complexes from weighted interaction graphs. By using graph topological patterns and biological properties as features, we model each complex subgraph by a probabilistic Bayesian network (BN). We use a training set of known complexes to learn the parameters of this BN model. The log-likelihood ratio derived from the BN is then used to score subgraphs in the protein interaction graph and identify new complexes. We applied our method to protein interaction data in yeast. As we show our algorithm achieved a considerable improvement over clique based algorithms in terms of its ability to recover known complexes. We discuss some of the new complexes predicted by our algorithm and determine that they likely represent true complexes.

Availability: Matlab implementation is available on the supporting website: www.cs.cmu.edu/~qyj/SuperComplex

Contact: zivbj{at}cs.cmu.edu


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