A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A massively parallel genetic algorithm for RNA secondary structure prediction
The Journal of Supercomputing
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Integrating Thermodynamic and Observed-Frequency Data for Non-coding RNA Gene Search
Transactions on Computational Systems Biology X
Predicting RNA secondary structure based on the class information and Hopfield network
Computers in Biology and Medicine
rnaDesign: local search for RNA secondary structure design
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
RNA pseudoknot prediction via an evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Bacterially inspired evolving system with an application to time series prediction
Applied Soft Computing
pEvoSAT: a novel permutation based genetic algorithm for solving the boolean satisfiability problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
RNA secondary structure prediction using conditional random fields model
International Journal of Data Mining and Bioinformatics
RNA Secondary Structure Prediction Using Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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This paper presents two in-depth studies on RnaPredict, an evolutionary algorithm for RNA secondary structure prediction. The first study is an analysis of the performance of two thermodynamic models, INN and INN-HB. The correlation between the free energy of predicted structures and the sensitivity is analyzed for 19 RNA sequences. Although some variance is shown, there is a clear trend between a lower free energy and an increase in true positive base pairs. With increasing sequence length, this correlation generally decreases. In the second experiment, the accuracy of the predicted structures for these 19 sequences are compared against the accuracy of the structures generated by the mfold dynamic programming algorithm (DPA) and also to known structures. RnaPredict is shown to outperform the minimum free energy structures produced by mfold and has comparable performance when compared to sub-optimal structures produced by mfold.