Heterodimeric protein complex identification
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Complex detection based on integrated properties
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
International Journal of Bioinformatics Research and Applications
A supervised approach to detect protein complex by combining biological and topological properties
International Journal of Data Mining and Bioinformatics
Identifying Pathway Proteins in Networks using Convergence
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Improving protein complex classification accuracy using amino acid composition profile
Computers in Biology and Medicine
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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@cs.cmu.edu