Title
A novel method for RNA secondary structure prediction
Abstract
In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold.
Year
DOI
Venue
2011
10.1109/ICNC.2011.6022235
ICNC
Keywords
Field
DocType
rnapredict,stem permutation problem,sarna-predict,psofold,rna secondary structure prediction,scs,solution conversion strategy,inertia weight,metaheuristics,particle swarm optimisation,learning factors,particle swarm optimization,fuzzy logic control,organic compounds,biology computing,molecular biophysics,molecular configurations,macromolecules,acrna,dynamic programming algorithm,improved pso,fuzzy control,dynamic programming,optimal solution search,adaptive pso parameter adjustment,ipso,optical fibers,frequency modulation,rna,sensitivity,fuzzy logic,objective function,optical fiber
Particle swarm optimization,Dynamic programming,Mathematical optimization,Computer science,Fuzzy logic,Permutation,Rna secondary structure prediction,Artificial intelligence,Inertia,Fuzzy control system,Machine learning,Metaheuristic
Conference
Volume
Issue
ISSN
2
null
2157-9555
ISBN
Citations 
PageRank 
978-1-4244-9950-2
1
0.36
References 
Authors
5
6
Name
Order
Citations
PageRank
Chong Xing120.72
Zhaohua Ji210.69
Gang Wang328265.93
Yao Wang42312.50
Wei Shen520.72
Yanchun Liang649563.74