Paper Title
Review on Machine Learning Approaches for Predicting RNA Folding
Abstract
Acquiring comprehensive understanding of base pairing within RNA secondary structure holds great promise for
advancing disease treatment and regulating cellular processes. In the pursuit of this goal, probabilistic models, particularly
those leveraging machine learning, have emerged as predominant in RNA secondary structure prediction. They have
demonstrated superior performance compared to earlier tools relying on comparative sequence analysis or folding algorithms
employing thermodynamic and stochastic parameter schemes. This review explores influential models utilizing machine
learning techniques that have significantly propelled research in RNA secondary structure prediction over the past two
decades. Additionally, the review aims to elucidate RNA types, functions, and their roles in disease mechanisms, providing
readers with essential knowledge to appreciate the importance of uncovering RNA structural information. To enhance
comprehension of the subject, an overview of alternative RNA structure tools and techniques is also included.
Keywords - RNA sequencing, RNA folding, transformer