title: Predicting interactions in protein networks by completing defective cliques.
abstract: Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/
  
  
  BACKGROUND
  Mark.Gerstein@yale.edu
  
  
  BACKGROUND
  Supplementary Materials are available at Bioinformatics online.
authors.affiliation: Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520-8285, USA.
authors.forename: Haiyuan
authors.initials: H
authors.lastname: Yu
authors.forename: Alberto
authors.initials: A
authors.lastname: Paccanaro
authors.forename: Valery
authors.initials: V
authors.lastname: Trifonov
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism