Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks  

作　　者：Kangkun Mao Jun Wang Yi Xiao 

机构地区：[1]School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Biophysics Reports》2020年第4期146-154,共9页生物物理学报（英文版）

基　　金：the National Natural Science Foundation of China(31570722).

摘　　要：Noncoding RNAs play important roles in cell and their secondary structures are vital for understanding their tertiary structures and functions.Many prediction methods of RNA secondary structures have been proposed but it is still challenging to reach high accuracy,especially for those with pseudoknots.Here we present a coupled deep learning model,called 2dRNA,to predict RNA secondary structure.It combines two famous neural network architectures bidirectional LSTM and U-net and only needs the sequence of a target RNA as input.Benchmark shows that our method can achieve state-of-the-art performance compared to current methods on a testing dataset.Our analysis also shows that 2dRNA can learn structural information from similar RNA sequences without aligning them.

关 键 词：RNA secondary structure prediction Deep learning Minimum free energy 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] Q522[自动化与计算机技术—控制科学与工程]