title: Protein complex identification by integrating protein-protein interaction evidence from multiple sources.
abstract: BACKGROUND
  Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.
  
  
  RESULTS
  We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.
  
  
  CONCLUSIONS
  We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China ; Department of Health Science Research, Mayo Clinic, Rochester, Minnesota, United States of America.
authors.forename: Bo
authors.initials: B
authors.lastname: Xu
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.affiliation: Department of Computer Science, Virginia Tech, Falls Church, Virginia, United States of America.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Department of Health Science Research, Mayo Clinic, Rochester, Minnesota, United States of America.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results