title: Inferring protein function by domain context similarities in protein-protein interaction networks.
abstract: BACKGROUND
  Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.
  
  
  RESULTS
  The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves.
  
  
  CONCLUSIONS
  This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods.
authors: MOE Key Laboratory of Bioinformatics, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, PR China. zhangsong04@mails.tsinghua.edu.cn
authors: Song
authors: S
authors: Zhang
authors: Hu
authors: H
authors: Chen
authors: Ke
authors: K
authors: Liu
authors: Zhirong
authors: Z
authors: Sun
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D020543:Proteome / Q000378:metabolism
chemical_list: D011506:Proteins; D020543:Proteome