TY - JOUR
T1 - Review of machine learning methods for RNA secondary structure prediction
AU - Zhao, Qi
AU - Zhao, Zheng
AU - Fan, Xiaoya
AU - Yuan, Zhengwei
AU - Mao, Qian
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2021 Zhao et al.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85113886334&partnerID=8YFLogxK
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U2 - 10.1371/journal.pcbi.1009291
DO - 10.1371/journal.pcbi.1009291
M3 - Review article
C2 - 34437528
AN - SCOPUS:85113886334
SN - 1553-734X
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8
M1 - e1009291
ER -