A survey of circular RNAs in complex diseases: databases, tools and computational methods

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Circular RNAs (circRNAs) are novel non-coding RNAs implicated in complex human diseases.
• Many circRNAs are involved in cancer progression, showing potential as diagnostic and therapeutic biomarkers.
• Understanding circRNA-disease relationships is crucial for elucidating disease pathogenesis and circRNA functions.

Purpose of the Study:

• To review databases, tools, and computational methods for predicting circRNA-disease associations.
• To summarize the functions and characteristics of circRNAs in tumorigenesis.
• To discuss future research directions in the field.

Main Methods:

• Review of existing literature, databases, and computational tools.
• Classification of prediction methods into network propagation, path-based, matrix factorization, deep learning, and other machine learning approaches.
• Analysis of circRNAs involved in cancer progression.

Main Results:

• Identification of numerous circRNAs associated with cancer.
• Overview of computational models for predicting circRNA-disease associations.
• Categorization of prediction methods highlights diverse algorithmic strategies.

Conclusions:

• Computational methods offer efficient alternatives to laborious experimental approaches for identifying circRNA-disease links.
• Accurate prediction of circRNA-disease associations can advance understanding of disease mechanisms and improve clinical trial efficiency.
• Further research is needed to address current challenges and refine predictive models.