title: A novel function prediction approach using protein overlap networks.
abstract: BACKGROUND
  Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database.
  
  
  RESULTS
  The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein.
  
  
  CONCLUSIONS
  The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.
authors.affiliation: Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka 565-0871, Japan. shideliang@ifrec.osaka-u.ac.jp
authors.forename: Shide
authors.initials: S
authors.lastname: Liang
authors.forename: Dandan
authors.initials: D
authors.lastname: Zheng
authors.forename: Daron M
authors.initials: DM
authors.lastname: Standley
authors.forename: Huarong
authors.initials: H
authors.lastname: Guo
authors.forename: Chi
authors.initials: C
authors.lastname: Zhang
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D023281:Genomics; D006801:Humans; D058977:Molecular Sequence Annotation; D011506:Proteins / Q000235:genetics / Q000378:metabolism*
chemical_list: D011506:Proteins