title: Faster and more accurate global protein function assignment from protein interaction networks using the MFGO algorithm.
abstract: BACKGROUND
  Predicting protein function accurately is an important issue in the post-genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co-expression profiles, and other information. Among these methods, the global optimization method (GOM) is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interactions network [Vazquez, A., Flammini, A., Maritan, A. and Vespignani, A. (2003) Global protein function prediction from protein-protein interaction networks, Nat. Biotechnol., 21, 697-700]. To boost both the accuracy and speed of GOM, a new prediction method, MFGO (modified and faster global optimization) is presented in this paper, which employs local optimal repetition method to reduce calculation time, and takes account of topological structure information to achieve a more accurate prediction.
  
  
  CONCLUSIONS
  On four proteins interaction datasets, including Vazquez dataset, YP dataset, DIP-core dataset, and SPK dataset, MFGO was tested and compared with the popular MR (majority rule) and GOM methods. Experimental results confirm MFGO's improvement on both speed and accuracy. Especially, MFGO method has a distinctive advantage in accurately predicting functions for proteins with few neighbors. Moreover, the robustness of the approach was validated both in a dataset containing a high percentage of unknown proteins and a disturbed dataset through random insertion and deletion. The analysis shows that a moderate amount of misplaced interactions do not preclude a reliable function assignment.
authors.affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China.
authors.forename: Shiwei
authors.initials: S
authors.lastname: Sun
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhao
authors.forename: Yishan
authors.initials: Y
authors.lastname: Jiao
authors.forename: Yifei
authors.initials: Y
authors.lastname: Yin
authors.forename: Lun
authors.initials: L
authors.lastname: Cai
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhang
authors.forename: Hongchao
authors.initials: H
authors.lastname: Lu
authors.forename: Runsheng
authors.initials: R
authors.lastname: Chen
authors.forename: Dongbo
authors.initials: D
authors.lastname: Bu
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D011506:Proteins / Q000502:physiology*; D029701:Saccharomyces cerevisiae Proteins / Q000502:physiology; D013997:Time Factors
chemical_list: D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins