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GCNSP: A Novel Prediction Method of Self-Interacting Proteins Based on Graph Convolutional Networks

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

As an essential protein interaction, self-interacting proteins (SIPs) plays a vital role in biological processes. Identifying and confirming SIPs is of great significance for the exploration of new gene functions, protein function research and proteomics research. Although a large number of SIPs have been confirmed with the rapid development of high-throughput technology, the biological experimental method is still limited by blindness and high cost, and has a high false-positive rate. Therefore, the use of computational techniques to accurately and efficiently predict SIPs has become an urgent need. In this study, a novel SIPs prediction method GCNSP based on Graph Convolutional Networks (GCN) is proposed. Firstly, the evolution information of protein is described by Position-Specific Scoring Matrix (PSSM). Then the feature information is extracted by GCN, and finally fed into Random Forest (RF) classifier for accurate classification. In the five-fold cross-validation on Human and Yeast data sets, GCNSP achieved 93.65% and 90.69% prediction accuracy with 99.64% and 99.08% specificity, respectively. In comparison with different classifier models and other existing methods, GCNSP shows strong competitiveness. The excellent results show that the proposed method is very suitable for SIPs prediction and can provide highly reliable candidates for biological experiments.

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Acknowledgements

This work is supported is supported in part by the National Natural Science Foundation of China, under Grants 61702444, in part by the West Light Foundation of The Chinese Academy of Sciences, under Grant 2018-XBQNXZ-B-008, in part by the Chinese Postdoctoral Science Foundation, under Grant 2019M653804, in part by the Tianshan youth - Excellent Youth, under Grant 2019Q029, in part by the Qingtan scholar talent project of Zaozhuang University. The authors would like to thank all anonymous reviewers for their constructive advices.

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Authors and Affiliations

  1. College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China

    Lei Wang

  2. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China

    Lei Wang, Zhu-Hong You & Zheng-Wei Li

  3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China

    Xin Yan

  4. School of Foreign Languages, Zaozhuang University, Zaozhuang, China

    Xin Yan

  5. School of Computer Science and Engineering, Central South University, Changsha, China

    Kai Zheng

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  1. Lei Wang
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  2. Zhu-Hong You
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  3. Xin Yan
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  4. Kai Zheng
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  5. Zheng-Wei Li
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Corresponding authors

Correspondence to Zhu-Hong You or Xin Yan .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Wang, L., You, ZH., Yan, X., Zheng, K., Li, ZW. (2020). GCNSP: A Novel Prediction Method of Self-Interacting Proteins Based on Graph Convolutional Networks. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_11

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_11

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  • Online ISBN: 978-3-030-60802-6

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