title: Functional evaluation of domain-domain interactions and human protein interaction networks.
abstract: BACKGROUND
  Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms.
  
  
  RESULTS
  Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.
authors: Andreas
authors: A
authors: Schlicker
authors: Carola
authors: C
authors: Huthmacher
authors: Fidel
authors: F
authors: Ramírez
authors: Thomas
authors: T
authors: Lengauer
authors: Mario
authors: M
authors: Albrecht
mesh_terms: D001665:Binding Sites; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D008956:Models, Chemical; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D011506:Proteins