title: Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.
abstract: 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.
authors.affiliation: Computational Biology, Informatics, Millennium Pharmaceuticals Inc., 40 Landsdowne Street, Cambridge, MA 02139, USA. joel.pradines@mpi.com
authors.forename: Joël R
authors.initials: JR
authors.lastname: Pradines
authors.forename: Victor
authors.initials: V
authors.lastname: Farutin
authors.forename: Steve
authors.initials: S
authors.lastname: Rowley
authors.forename: Vlado
authors.initials: V
authors.lastname: Dancík
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D016012:Poisson Distribution; D011506:Proteins / Q000502:physiology*
chemical_list: D011506:Proteins