Protein complex identification by supervised graph local clustering

@article{citeulike:2938453, 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@cs.cmu.edu 10.1093/bioinformatics/btn164}, author = {Qi, Yanjun and Balem, Fernanda and Faloutsos, Christos and Klein-Seetharaman, Judith and Bar-Joseph, Ziv }, citeulike-article-id = {2938453}, doi = {http://dx.doi.org/10.1093/bioinformatics/btn164}, journal = {Bioinformatics}, keywords = {bioinformatics, clustering, complex, graph, interaction, protein-protein}, month = {July}, number = {13}, pages = {i250--268}, posted-at = {2008-06-28 01:53:59}, priority = {2}, title = {Protein complex identification by supervised graph local clustering}, url = {http://dx.doi.org/10.1093/bioinformatics/btn164}, volume = {24}, year = {2008} }

TY - JOUR ID - citeulike:2938453 L3 - citeulike-article-id:2938453 TI - Protein complex identification by supervised graph local clustering JF - Bioinformatics VL - 24 IS - 13 SP - i250 EP - 268 N2 - 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 10.1093/bioinformatics/btn164 KW - bioinformatics KW - clustering KW - complex KW - graph KW - interaction KW - protein-protein AU - Qi, Yanjun AU - Balem, Fernanda AU - Faloutsos, Christos AU - Klein-Seetharaman, Judith AU - Bar-Joseph, Ziv PY - 2008/07/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btn164 ER -