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An Ensemble Method for Predicting Subnuclear Localizations from Primary Protein Structures

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  • Guo Sheng Han
  • Zu Guo Yu
  • Vo Anh
  • Anaththa P D Krishnajith
  • Yu-Chu Tian

Abstract

Background: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. Methodology/Principal Findings: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. Conclusions: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method. It is freely available at http://bioinformatics.awowshop.com/snlpred_page.php.

Suggested Citation

  • Guo Sheng Han & Zu Guo Yu & Vo Anh & Anaththa P D Krishnajith & Yu-Chu Tian, 2013. "An Ensemble Method for Predicting Subnuclear Localizations from Primary Protein Structures," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-14, February.
  • Handle: RePEc:plo:pone00:0057225
    DOI: 10.1371/journal.pone.0057225
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    References listed on IDEAS

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    1. Yu, Zu-Guo & Anh, Vo & Lau, Ka-Sing, 2004. "Fractal analysis of measure representation of large proteins based on the detailed HP model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 337(1), pages 171-184.
    2. Robert D. Phair & Tom Misteli, 2000. "High mobility of proteins in the mammalian cell nucleus," Nature, Nature, vol. 404(6778), pages 604-609, April.
    3. Kuo-Chen Chou & Hong-Bin Shen, 2010. "A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-9, April.
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    1. Sun, Bingbin & Yao, Jialing & Xi, Lifeng, 2019. "Eigentime identities of fractal sailboat networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 338-349.

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