title: Semantically predicting protein functions based on protein functional connectivity.
abstract: BACKGROUND
  The current availability of public protein-protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure.
  
  
  RESULTS
  An approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches.
  
  
  CONCLUSIONS
  Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.
authors: Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. w6zhu@students.latrobe.edu.au
authors: Wei
authors: W
authors: Zhu
authors: Jingyu
authors: J
authors: Hou
authors: Yi-Ping Phoebe
authors: YP
authors: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins