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Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization

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  • Junzhe Cao
  • Wenqi Liu
  • Jianjun He
  • Hong Gu

Abstract

Subcellular localization of a protein is important to understand proteins’ functions and interactions. There are many techniques based on computational methods to predict protein subcellular locations, but it has been shown that many prediction tasks have a training data shortage problem. This paper introduces a new method to mine proteins with non-experimental annotations, which are labeled by non-experimental evidences of protein databases to overcome the training data shortage problem. A novel active sample selection strategy is designed, taking advantage of active learning technology, to actively find useful samples from the entire data pool of candidate proteins with non-experimental annotations. This approach can adequately estimate the “value” of each sample, automatically select the most valuable samples and add them into the original training set, to help to retrain the classifiers. Numerical experiments with for four popular multi-label classifiers on three benchmark datasets show that the proposed method can effectively select the valuable samples to supplement the original training set and significantly improve the performances of predicting classifiers.

Suggested Citation

  • Junzhe Cao & Wenqi Liu & Jianjun He & Hong Gu, 2013. "Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
  • Handle: RePEc:plo:pone00:0067343
    DOI: 10.1371/journal.pone.0067343
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    References listed on IDEAS

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    1. Jianjun He & Hong Gu & Wenqi Liu, 2012. "Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-10, June.
    2. 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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