Review of machine learning methods for RNA secondary structure prediction

Qi Zhao, Zheng Zhao, Xiaoya Fan, Zhengwei Yuan, Qian Mao, Yudong Yao

Research output: Contribution to journalReview articlepeer-review

43 Scopus citations

Abstract

Secondary structure plays an important role in determining the function of noncoding 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 (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Original languageEnglish
Article numbere1009291
JournalPLoS Computational Biology
Volume17
Issue number8
DOIs
StatePublished - Aug 2021

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