ArticleBMC bioinformatics2012
Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Subcellular localization as a driver of protein function.Nature reviews. Molecular cell biology · 2026Review
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Co-evolution of gene transfer agents and their alphaproteobacterial hosts.Journal of bacteriology · 2024Article
- Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants.Advances in experimental medicine and biology · 2021Article
- Functional expression and impact of testis-specific gene antigen 10 in breast cancer: a combined in vitro and in silico analysis.BioImpacts : BI · 2019Article
- Interactome of the Autoimmune Risk Protein ANKRD55.Frontiers in immunology · 2019Article
- PMLPR: A novel method for predicting subcellular localization based on recommender systems.Scientific reports · 2018Article
- Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.PLoS computational biology · 2016Article
- Improved multi-level protein-protein interaction prediction with semantic-based regularization.BMC bioinformatics · 2014Article
- Joint probabilistic-logical refinement of multiple protein feature predictors.BMC bioinformatics · 2014Article
- Article
- Proteome-wide discovery of mislocated proteins in cancer.Genome research · 2013Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProteins 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.
resultsWe 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.
conclusionsPhysical 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.
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