  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.

Biomolecules;Machine Learning;Machine Learning