Computational prediction of RNA tertiary structures using machine learning methods*

被引:6
|
作者
Huang, Bin [1 ,2 ]
Du, Yuanyang [1 ,2 ]
Zhang, Shuai [1 ,2 ]
Li, Wenfei [1 ,2 ]
Wang, Jun [1 ,2 ]
Zhang, Jian [1 ,2 ]
机构
[1] Nanjing Univ, Natl Lab Solid State Microstruct, Sch Phys, Collaborat Innovat Ctr Adv Microstruct, Nanjing 210093, Peoples R China
[2] Nanjing Univ, Inst Brain Sci, Kuang Yaming Honors Sch, Nanjing 210093, Peoples R China
基金
中国国家自然科学基金;
关键词
RNA structure prediction; RNA scoring function; knowledge-based potentials; machine learning; convolutional neural networks; 3D; POTENTIALS; PUZZLES; IDENTIFICATION; SIMULATIONS; SECONDARY; VARIANTS; NETWORKS; MODULES;
D O I
10.1088/1674-1056/abb303
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页数:7
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