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PSI: A Comprehensive and Integrative Approach for Accurate Plant Subcellular Localization Prediction

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  • Lili Liu
  • Zijun Zhang
  • Qian Mei
  • Ming Chen

Abstract

Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ∼10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/.

Suggested Citation

  • Lili Liu & Zijun Zhang & Qian Mei & Ming Chen, 2013. "PSI: A Comprehensive and Integrative Approach for Accurate Plant Subcellular Localization Prediction," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    • Handle: RePEc:plo:pone00:0075826
    DOI: 10.1371/journal.pone.0075826
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    References listed on IDEAS

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    1. Kuo-Chen Chou & Hong-Bin Shen, 2010. "Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization," PLOS ONE, Public Library of Science, vol. 5(6), pages 1-11, June.
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    1. Shibiao Wan & Man-Wai Mak & Sun-Yuan Kung, 2014. "HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of Multi-Location Proteins," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-12, March.

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