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• W4367675086 endingPage "106352" @default.
• W4367675086 startingPage "106352" @default.
• W4367675086 abstract "Circular ribonucleic acids (circRNAs) are widely expressed in cells and tissues and play vital roles in cellular physiological processes. Their expressions are associated with clinicopathological features in cancer patients. Thus, they act as molecular biomarkers for tumor diagnosis, non-invasive monitoring, prognosis, and therapeutic intervention. Recent research has shown that circRNAs can interact with RNA-binding proteins (RBPs), which is a critical aspect for understanding circRNA functions. In this paper, we review the state-of-the-art deep learning and ensemble deep learning methods highlighting their strengths and weaknesses in circRNA-RBP interaction prediction. We further discuss new strategies for improving the existing methods. The existing circRNA-RBP interaction prediction methods are further classified as deep learning or ensemble deep learning. Moreover, we elaborate on the critical factors for cicRNA-RBP interactions, which can help the development of prediction models by providing necessary clarifications. This review further presents the benefits of using ensemble deep learning methods over single deep learning methods. Prediction performance improvements of ensemble deep learning methods over single deep learning methods are observed and the reasons for those improvements are discussed. Furthermore, this review discusses open problems of this research field and provides recommendations on future research directions." @default.
• W4367675086 created "2023-05-03" @default.
• W4367675086 creator A5014847381 @default.
• W4367675086 creator A5028632692 @default.
• W4367675086 creator A5053524109 @default.
• W4367675086 date "2023-08-01" @default.
• W4367675086 modified "2023-10-17" @default.
• W4367675086 title "Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review" @default.
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• W4367675086 doi "https://doi.org/10.1016/j.engappai.2023.106352" @default.
• W4367675086 hasPublicationYear "2023" @default.
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