Provider: Silverchair
Database: Oxford University Press
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TY  - JOUR
AU  - Chen, Yaojia
AU  - Wang, Jiacheng
AU  - Wang, Chuyu
AU  - Liu, Mingxin
AU  - Zou, Quan
T1  - Deep learning models for disease-associated circRNA prediction: a review
PY  - 2022
Y1  - 2022/09/21
DO  - 10.1093/bib/bbac364
JO  - Briefings in Bioinformatics
JA  - Brief Bioinform
SP  - bbac364
SN  - 1477-4054
AB  - 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.
Y2  - 10/9/2022
UR  - https://doi.org/10.1093/bib/bbac364
ER  -