title: Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.
abstract: BACKGROUND
  Proteins that interact in vivo tend to reside within the same or "adjacent" subcellular compartments. This observation provides opportunities to reveal protein subcellular localization in the context of the protein-protein interaction (PPI) network. However, so far, only a few efforts based on heuristic rules have been made in this regard.
  
  
  RESULTS
  We systematically and quantitatively validate the hypothesis that proteins physically interacting with each other probably share at least one common subcellular localization. With the result, for the first time, four graph-based semi-supervised learning algorithms, Majority, χ2-score, GenMultiCut and FunFlow originally proposed for protein function prediction, are introduced to assign "multiplex localization" to proteins. We analyze these approaches by performing a large-scale cross validation on a Saccharomyces cerevisiae proteome compiled from BioGRID and comparing their predictions for 22 protein subcellular localizations. Furthermore, we build an ensemble classifier to associate 529 unlabeled and 137 ambiguously-annotated proteins with subcellular localizations, most of which have been verified in the previous experimental studies.
  
  
  CONCLUSIONS
  Physical interaction of proteins has actually provided an essential clue for their co-localization. Compared to the local approaches, the global algorithms consistently achieve a superior performance.
authors: School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, PR China.
authors: Jonathan Q
authors: JQ
authors: Jiang
authors: Maoying
authors: M
authors: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D060066:Protein Interaction Maps; D011506:Proteins / Q000032:analysis*; D020543:Proteome / Q000032:analysis; D012441:Saccharomyces cerevisiae / Q000378:metabolism
chemical_list: D011506:Proteins; D020543:Proteome