Review of Machine-Learning Methods for RNA Secondary Structure Prediction
This is an incremental review that synthesizes existing methods for researchers in computational biology and bioinformatics working on RNA structure prediction.
This review paper addresses the stagnation in computational RNA secondary structure prediction performance by surveying machine learning methods, particularly deep learning approaches, that have recently emerged to improve prediction accuracy using increased RNA structure data availability.
Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed.