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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
Published on: October 21, 2022
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Deep learning models for disease-associated circRNA prediction: a review.
Yaojia Chen1, Jiacheng Wang2, Chuyu Wang3
1College 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.
Briefings in Bioinformatics
|September 21, 2022
Summary
Deep learning models show promise for predicting circular RNA (circRNA) and disease associations, offering a faster alternative to traditional experiments. This review covers circRNA databases and deep learning methods for disease prediction.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Circular RNAs (circRNAs) are increasingly recognized for their roles in disease pathogenesis.
- Traditional experimental methods for identifying circRNA-disease associations are costly and time-consuming.
- Deep learning offers powerful representation learning for efficient prediction.
Conclusions:
- Deep learning is a promising technology for predicting circRNA-disease associations.
- Different deep learning methods have distinct advantages and limitations.
- Further research is needed to optimize these models for clinical applications.
