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Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13798))

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Abstract

The Hi-C data is the basis of three-dimensional chromosome structure reconstruction. In this paper, a method integrating U-Net and ResNET network is proposed to infer high resolution Hi-C data from existing low-resolution Hi-C data. Firstly, the U-Net is used as the backbone network to make full use of the shallow layer details and deep layer essential features of Hi-C data. Secondly, the residual module is introduced into the coding path and decoding path of the network to increase the depth of the model and prevent network degradation. Finally, the down sampling is used to reduce the calculation amount to decrease the required running time for the network model. The experimental results on real datasets showed that the proposed method had better performance than existing methods, and the inferred high resolution Hi-C data was closer to the real Hi-C data.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 61962004, and the Innovation Project of Guangxi Graduate Education (No.YCSW2021020).

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Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, Guangxi, China

    Fengjiao Zhao, Na Li & Cheng Zhong

  2. Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities, Nanning, 530004, Guangxi, China

    Fengjiao Zhao, Na Li & Cheng Zhong

Authors
  1. Fengjiao Zhao
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  2. Na Li
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  3. Cheng Zhong
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Corresponding author

Correspondence to Cheng Zhong .

Editor information

Editors and Affiliations

  1. Tohoku University, Aoba-ku, Japan

    Hiroyuki Takizawa

  2. Sun Yat-sen University, Guangzhou, China

    Hong Shen

  3. The University of Tokyo, Tokyo, Japan

    Toshihiro Hanawa

  4. Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    Jong Hyuk Park

  5. Griffith University, Queensland, QLD, Australia

    Hui Tian

  6. Tokyo Denki University, Tokyo, Japan

    Ryusuke Egawa

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Zhao, F., Li, N., Zhong, C. (2023). Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks. In: Takizawa, H., Shen, H., Hanawa, T., Hyuk Park, J., Tian, H., Egawa, R. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2022. Lecture Notes in Computer Science, vol 13798. Springer, Cham. https://doi.org/10.1007/978-3-031-29927-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-29927-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29926-1

  • Online ISBN: 978-3-031-29927-8

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