The significance of thermodynamic models in the accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

2007 ◽  
Author(s):  
Herbert H. Tsang ◽  
Kay C. Wiese
Keyword(s):  
Simulated Annealing ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Thermodynamic Models ◽  
Accuracy Improvement ◽  
Rna Secondary Structure Prediction

scholarly journals UFold: Fast and Accurate RNA Secondary Structure Prediction with Deep Learning

2020 ◽  
Author(s):  
Yingxin Cao ◽  
Laiyi Fu ◽  
Jie Wu ◽  
Qing Nie ◽  
Xiaohui Xie
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Correction Function ◽  
Thermodynamic Models ◽  
Rna Secondary Structure Prediction ◽  
Rna Molecules ◽  
Online Web

AbstractFor many RNA molecules, the secondary structure is essential for the correction function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization. Here we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 31% improvement over traditional thermodynamic models and 24.5% improvement over other learning-based methods. It achieves an F1 score of 0.96 on base pair prediction accuracy. An online web server running UFold is publicly available at http://ufold.ics.uci.edu.

Sign in / Sign up

Export Citation Format

Share Document