Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

Abstract Background RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for...

Full description

Bibliographic Details
Main Authors: Weizhong Lu, Ye Tang, Hongjie Wu, Hongmei Huang, Qiming Fu, Jing Qiu, Haiou Li
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Bioinformatics
Subjects:
RNA
Secondary structure prediction
Recurrent neural network
LSTM
Pseudoknots
Online Access:https://doi.org/10.1186/s12859-019-3258-7

Internet

https://doi.org/10.1186/s12859-019-3258-7

Similar Items