  RNAs play crucial and versatile roles in biological processes. Computational
prediction approaches can help to understand RNA structures and their
stabilizing factors, thus providing information on their functions, and
facilitating the design of new RNAs. Machine learning (ML) techniques have made
tremendous progress in many fields in the past few years. Although their usage
in protein-related fields has a long history, the use of ML methods in
predicting RNA tertiary structures is new and rare. Here, we review the recent
advances of using ML methods on RNA structure predictions and discuss the
advantages and limitation, the difficulties and potentials of these approaches
when applied in the field.
