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GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph Attention Network

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Intelligent Computing Theories and Application (ICIC 2022)

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Abstract

With the deepening of research, we can find that circular RNAs (circRNAs) have important effects on many human physiological and pathological pathways. Studying the association of circRNAs with diseases not only helps to study biological processes, but also provides new directions for the diagnosis and treatment of diseases. However, it is relatively inefficient to verify the association of circRNAs with diseases only by biotechnology. This paper proposed a computational method GATSDCD based on graph attention network (GAT) and neural network (NN) to predict associations between circRNAs-diseases. In GATSDCD, it combined similarity features and semantic features of circRNAs and diseases as raw features. Then, we denoised the original features using singular value matrix decomposition to better represent circRNAs and diseases. Further, using the obtained circRNA and disease features as node attributes, a graph attention network was used to construct feature vectors in subgraphs to extract deep embedded features. Finally, a neural network was applied to make predictions about potential associations. The experimental results showed that the GATSDCD model outperforms existing methods in multiple aspects, and is an effective method to identify circRNA-disease associations. Case study also demonstrated that GATSDCD can effectively identify circRNAs associated with gastric and breast cancers.

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Acknowledgments

The work was supported by the National Natural Science Foundation of China (No. 62131004, No.61922020, No.61872114), the Sichuan Provincial Science Fund for Distinguished Young Scholars (2021JDJQ0025), and the Special Science Foundation of Quzhou (2021D004).

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Authors and Affiliations

  1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China

    Mengting Niu & Quan Zou

  2. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China

    Mengting Niu & Quan Zou

  3. Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt

    Abd El-Latif Hesham

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  1. Mengting Niu
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  2. Abd El-Latif Hesham
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  3. Quan Zou
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The authors have declared no competing interests.

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Correspondence to Quan Zou .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Niu, M., Hesham, A.EL., Zou, Q. (2022). GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph Attention Network. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

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