Circular RNAs and complex diseases: from experimental results to computational models

• Published in: Briefings in bioinformatics Vol. 22; no. 6
• Main Authors: Wang, Chun-Chun, Han, Chen-Di, Zhao, Qi, Chen, Xing
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 05.11.2021
• Subjects: Algorithms; Computational Biology - methods; Databases, Genetic; Female; Humans; Machine Learning; Models, Genetic; Neoplasms - genetics; Review; RNA, Circular - genetics; circRNA-disease association prediction; computational model; network algorithm; disease; circRNA; machine learning
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbab286

Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.