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Prediction of Membrane Protein Interaction Based on Deep Residual Learning

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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

The interaction of membrane proteins in organisms is directly related to their functions. Accurate prediction of membrane protein interaction can better predict its spatial structure. Therefore, it is of great significance to study the interaction of membrane proteins. Currently, there is no contact method specifically for membrane protein prediction. In this paper, a membrane protein prediction tool based on deep residual learning is established. Combined with the transformation of the covariance matrix, it can well predict the interaction of membrane proteins. Compared with other methods, the experimental data and results of this model are more accurate.

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Acknowledgement

This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371, and 61876217), Jiangsu Province 333 Talent Project, Top Talent Project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610, and SZS201609).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Tengsheng Jiang, Hongjie Wu, Yuhui Chen, Haiou Li, Jin Qiu, Weizhong Lu & Qiming Fu

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  1. Tengsheng Jiang
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  2. Hongjie Wu
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  3. Yuhui Chen
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  4. Haiou Li
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  5. Jin Qiu
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  6. Weizhong Lu
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  7. Qiming Fu
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Corresponding author

Correspondence to Hongjie Wu .

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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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Jiang, T. et al. (2020). Prediction of Membrane Protein Interaction Based on Deep Residual Learning. 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_10

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60801-9

  • Online ISBN: 978-3-030-60802-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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