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Link to original content: https://doi.org/10.1007/s11704-024-40060-2
Computational approaches for circRNA-disease association prediction: a review | Frontiers of Computer Science
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Computational approaches for circRNA-disease association prediction: a review

  • Review Article
  • Open access
  • Published: 22 November 2024
  • Volume 19, article number 194904, (2025)
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Frontiers of Computer Science Aims and scope Submit manuscript
Computational approaches for circRNA-disease association prediction: a review
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  • Mengting Niu1,2,
  • Yaojia Chen3,4,
  • Chunyu Wang5,
  • Quan Zou3,4 &
  • …
  • Lei Xu2 

  • 125 Accesses

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Abstract

Circular RNA (circRNA) is a covalently closed RNA molecule formed by back splicing. The role of circRNAs in posttranscriptional gene regulation provides new insights into several types of cancer and neurological diseases. CircRNAs are associated with multiple diseases and are emerging biomarkers in cancer diagnosis and treatment. The associations prediction is one of the current research hotspots in the field of bioinformatics. Although research on circRNAs has made great progress, the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time. Fortunately, advances in computational methods have made considerable progress in circRNA research. This review comprehensively discussed the functions and databases related to circRNA, and then focused on summarizing the calculation model of related predictions, detailed the mainstream algorithm into 4 categories, and analyzed the advantages and limitations of the 4 categories. This not only helps researchers to have overall understanding of circRNA, but also helps researchers have a detailed understanding of the past algorithms, guide new research directions and research purposes to solve the shortcomings of previous research.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 62231013, 62201129, 62303328, 62302341, 62271329, 62372332), the National Key R&D Program of China (2022ZD0117700), the National funded postdoctoral researcher program of China (GZC20230382), the Shenzhen Polytechnic University Research Fund (6024310027K, 6022310036K, 6023310037K), the Key Field of Department of Education of Guangdong Province (2022ZDZX2082), and the Special Science Foundation of Quzhou (2023D036). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Mengting Niu

  2. School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic University, Shenzhen, 518055, China

    Mengting Niu & Lei Xu

  3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Yaojia Chen & Quan Zou

  4. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China

    Yaojia Chen & Quan Zou

  5. Faculty of Computing, Harbin Institute of Technology, Harbin, 150006, China

    Chunyu Wang

Authors
  1. Mengting Niu
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  2. Yaojia Chen
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  3. Chunyu Wang
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  4. Quan Zou
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  5. Lei Xu
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Corresponding author

Correspondence to Lei Xu.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Additional information

Mengting Niu is a postdoctoral fellow at University of Electronic Science and Technology of China and Shenzhen Polytechnic University, China. Her research interests include bioinformatics, data mining, and biomedicine.

Yaojia Chen is a PhD candidate at the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China. Her research interests include machine learning and bioinformatics.

Chunyu Wang is a professor at Faculty of Computing, Harbin Institute of Technology, China. His research fields include computational biology and machine learning, especially on the structure and function prediction of biomolecules, artificial intelligence-assisted drug discovery, high-throughput sequence data analysis etc.

Quan Zou received the BSc, MSc, and the PhD degrees in computer science from the Harbin Institute of Technology, China in 2004, 2007, and 2009, respectively. He is currently a professor with the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China. His research is in the areas of bioinformatics, machine learning, and parallel computing. Several related works have been published by Science, Briefings in Bioinformatics, Bioinformatics, the IEEE/ACM Transactions on Computational Biology and Bioinformatitcs, etc. He is the editor-inchief of Current Bioinformatics, associate editor of IEEE Access, and an editorial board member of Computers in Biology and Medicine, Genes, Scientific Reports, etc.

Lei Xu is an associate professor at the School of Electronic and Communication Engineering, Shenzhen Polytechnic, China. She received her BSc and MSc from the School of Computer Science and Technology in Harbin Institute of Technology, China in 2006 and 2008, respectively. She got her PhD degree from the Department of Computing, The Hong Kong Polytechnic University, China in 2013. Her research interests are focused on bioinformatics, pattern recognition.

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Cite this article

Niu, M., Chen, Y., Wang, C. et al. Computational approaches for circRNA-disease association prediction: a review. Front. Comput. Sci. 19, 194904 (2025). https://doi.org/10.1007/s11704-024-40060-2

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  • Received: 11 January 2024

  • Accepted: 21 April 2024

  • Published: 22 November 2024

  • DOI: https://doi.org/10.1007/s11704-024-40060-2

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Keywords

  • circular RNA
  • disease association prediction
  • machine learning
  • data mining
  • deep learning
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