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This article is part of the supplement: Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)

Open Access Proceedings

Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study

Jonathan Q Jiang12 and Maoying Wu1*

Author Affiliations

1 School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, P.R.China

2 Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

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BMC Bioinformatics 2012, 13(Suppl 10):S20  doi:10.1186/1471-2105-13-S10-S20

Published: 25 June 2012



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.


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.


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.