Review of Machine-Learning Methods for RNA Secondary Structure Prediction

TL;DR

This review discusses the evolution of machine-learning methods, especially deep learning, for RNA secondary structure prediction, highlighting recent advances, current challenges, and future research directions in the field.

Contribution

It provides a comprehensive overview and a tabular summary of machine-learning approaches for RNA secondary structure prediction, emphasizing recent developments and future trends.

Findings

Deep learning methods have improved RNA structure prediction accuracy.

Performance of computational methods has stagnated over the past decade.

Emerging machine-learning techniques are addressing current challenges.

Abstract

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.