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An Efficient Algorithm Based on Hopfield Neural Network for RNA Secondary Structure Prediction


Yanqiu Che, Zheng Tang, Shaozhi Liu


Vol. 7  No. 5  pp. 49-54


During the past years, many improvements have been made in computational methods for the prediction of RNA secondary structure to provide insight into many functions RNAs perform in biology processes. In this paper, we propose an efficient algorithm that can increase energy temporarily and then energy declines again by introducing stochastic dynamics into the Hopfield neural network. The proposed algorithm can help the Hopfield network escape from local minima and find the optimal or near-optimal solutions. The proposed algorithm has been applied to RNA secondary structure prediction problem. Simulation results verify that it has the ability to search the more stable RNA secondary structure for an RNA sequence compared to other neural network methods.


RNA secondary structure prediction, Hopfield neural network, stochastic dynamics