Deep learning models for disease-associated circRNA prediction: a review.

Journal: Briefings in bioinformatics
Published Date: Nov 19, 2022

Abstract

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

Authors

  • Yaojia Chen
    College of Electronics and Information Engineering Guangdong Ocean University, Zhanjiang, China and the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
  • Jiacheng Wang
    Vanderbilt University, Nashville TN 37215, USA.
  • Chuyu Wang
    Faculty of Computing, Harbin Institute of Technology, Harbin, China.
  • Mingxin Liu
    College of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, China.
  • Quan Zou

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