Applied Intelligence and Computing
Computational Approaches for Predicting circRNA-Disease Associations: A Comprehensive Review and Integration of Methods
Authors: Aiswarya Mohan, Deepthi K and Arun TM
Publishing Date: 10-11-2024
ISBN: 978-81-955020-9-7
Abstract
Circular RNA (circRNA), a class of non-coding RNA, produced through the back-splicing of a pre-mRNA, has emerged as a promising way to understand the complex molecular pathways involved in various diseases. These peculiar non-coding RNAs play diverse roles in regulating gene expression, cellular communication, and protein translation, thereby substantially influencing disease pathogenesis. Their involvement in disease initiation and progression underscores the significance of exploring the associations between circRNAs and particular diseases. Exploring the possibilities of circular RNAs can lead to new strategies for diagnosing and treating newly emerging diseases. In contrast to traditional costly biological experiments for revealing associations between circular RNAs and diseases, novel and more efficient computational approaches are being developed to uncover the complex relationships between circRNAs and diseases. Comprehending these associations offer vital insights into disease mechanisms, that can contribute to disease diagnostic and therapeutic strategies. This paper analyses eleven fundamental studies investigating circRNAdisease associations using advanced computational techniques, including network- based, matrix-based and machine learning methods. Further- more, the study concludes with a comprehensive listing of the Area Under the Curve (AUC) values from each of the studies, providing a quantitative assessment of the predictive performance of the computational models. The findings from these studies lay the groundwork for developing novel diagnostic and therapeutic approaches for a broad spectrum of human diseases that target explicitly specific circular RNAs.
Keywords
CircRNA, Associations, Disease, Matrix- completion, Machine learning, Network analysis.
Cite as
Aiswarya Mohan, Deepthi K and Arun TM, "Computational Approaches for Predicting circRNA-Disease Associations: A Comprehensive Review and Integration of Methods", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 195-208. https://doi.org/10.56155/978-81-955020-9-7-20