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Journal of Computational Biology
Analyzing Protein Lists with Large Networks: Edge-Count Probabilities in Random Graphs with Given Expected Degrees
To cite this article:
Joël R. Pradines, Victor Farutin, Steve Rowley, Vlado Dančík.
Journal of Computational Biology.
March 2005,
12(2): 113-128.
doi:10.1089/cmb.2005.12.113.
Joël R. Pradines Computational Biology, Informatics, Millennium Pharmaceuticals Inc., 40 Landsdowne Street, Cambridge, MA 02139. Victor Farutin Computational Biology, Informatics, Millennium Pharmaceuticals Inc., 40 Landsdowne Street, Cambridge, MA 02139. Steve Rowley Computational Biology, Informatics, Millennium Pharmaceuticals Inc., 40 Landsdowne Street, Cambridge, MA 02139. Vlado Dančík Computational Biology, Informatics, Millennium Pharmaceuticals Inc., 40 Landsdowne Street, Cambridge, MA 02139. We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.  This paper was cited by:Edge-count probabilities for the identification of local protein communities and their organization Victor Farutin, Keith Robison, Eric Lightcap, Vlado Dancik, Alan Ruttenberg, Stanley Letovsky, Joel Pradines Proteins: Structure, Function, and Bioinformatics. Mar 2006, Vol. 62, No. 3: 800-818 CrossRef |
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