The associations prediction is one of the current research hotspots in the field of bioinformatics. Although research on circRNAs has made great progress, the traditional biological method of verifying circRNA-disease associations is still a great challenge because it is a difficult task and requires much time. Fortunately, advances in computational methods have made considerable progress in circRNA research.

To solve the problems, a research team led by Lei Xu published their new review on 15 Apr 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

This review comprehensively discussed the functions and databases related to circRNA, and then focused on summarizing the calculation model of related predictions, detailed the mainstream algorithm into 4 categories, and analyzed the advantages and limitations of the 4 categories. This not only helps researchers to have overall understanding of circRNA, but also helps researchers have a detailed understanding of the past algorithms, guide new research directions and research purposes to solve the shortcomings of previous research.

This paper provides a brief overview of some available public databases, algorithms and tools developed to predict circRNA-disease associations. Although these algorithms and tools have achieved certain results, there are still some shortcomings, and more in-depth research and improvement are needed in the future. (1) There is an imbalance of circRNA disease association positive samples versus negative samples. (2) Most of the current computational methods predict circRNA disease associations that are covered in known association datasets; they simply predict fewer novel circRNA disease associations and do not detect diseases associated with novel circRNAs well. (3) Existing computational models are mostly based on incompletely related biological information, there is redundancy and noise between data, and circRNA similarity and disease similarity cannot be sufficiently fused. (4) The generalizable performance of the prediction model was not validated on other circRNA disease association datasets, and computational models cannot infer new circRNAs without any relationship to disease.

Then, this review also point out some important issues in current research and outline several research directions worthy of advancing computational associations.

DOI: 10.1007/s11704-024-40060-2