title: Dense module enumeration in biological networks.
abstract: Automatic discovery of functional complexes from protein interaction data is a rewarding but challenging problem. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically detect dense modules with interesting profiles. Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way.
authors.affiliation: AIST Computational Biology Research Center, Tokyo, Japan. koji.tsuda@aist.go.jp
authors.forename: Koji
authors.initials: K
authors.lastname: Tsuda
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Georgii
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D015870:Gene Expression; D020869:Gene Expression Profiling; D010641:Phenotype; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism
chemical_list: D011506:Proteins