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Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties

Author

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  • Lele Hu
  • Tao Huang
  • Xiaohe Shi
  • Wen-Cong Lu
  • Yu-Dong Cai
  • Kuo-Chen Chou

Abstract

Background: With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner. Methodology/Principal Findings: Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%. Conclusions/Significance: The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.

Suggested Citation

  • Lele Hu & Tao Huang & Xiaohe Shi & Wen-Cong Lu & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0014556
    DOI: 10.1371/journal.pone.0014556
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    References listed on IDEAS

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    1. David Eisenberg & Edward M. Marcotte & Ioannis Xenarios & Todd O. Yeates, 2000. "Protein function in the post-genomic era," Nature, Nature, vol. 405(6788), pages 823-826, June.
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    Cited by:

    1. Guohua Huang & Yuchao Zhang & Lei Chen & Ning Zhang & Tao Huang & Yu-Dong Cai, 2014. "Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    2. Lei Chen & Jing Yang & Zhihao Xing & Fei Yuan & Yang Shu & YunHua Zhang & XiangYin Kong & Tao Huang & HaiPeng Li & Yu-Dong Cai, 2017. "An integrated method for the identification of novel genes related to oral cancer," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-25, April.
    3. Lei Chen & Wei-Ming Zeng & Yu-Dong Cai & Kai-Yan Feng & Kuo-Chen Chou, 2012. "Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    4. Lei Chen & Chen Chu & Xiangyin Kong & Guohua Huang & Tao Huang & Yu-Dong Cai, 2015. "A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    5. Dechao Tian & Kwok Pui Choi, 2013. "Sharp Bounds and Normalization of Wiener-Type Indices," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    6. Lei Chen & Jing Lu & Jian Zhang & Kai-Rui Feng & Ming-Yue Zheng & Yu-Dong Cai, 2013. "Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
    7. Yu-Fei Gao & Lei Chen & Yu-Dong Cai & Kai-Yan Feng & Tao Huang & Yang Jiang, 2012. "Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.

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