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Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

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  • Jianjun He
  • Hong Gu
  • Wenqi Liu

Abstract

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0037155
    DOI: 10.1371/journal.pone.0037155
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    References listed on IDEAS

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    1. Zhisong He & Jian Zhang & Xiao-He Shi & Le-Le Hu & Xiangyin Kong & Yu-Dong Cai & Kuo-Chen Chou, 2010. "Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-8, March.
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    Cited by:

    1. 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.
    2. Chi-Hua Tung & Chi-Wei Chen & Han-Hao Sun & Yen-Wei Chu, 2017. "Predicting human protein subcellular localization by heterogeneous and comprehensive approaches," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-14, June.

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