RNA Secondary Structure Prediction Based on Forest Representation and Genetic Algorithm

RNA secondary structure prediction is one of the most important research areas in bioinformatics. This paper presents an improved genetic algorithm to predict RNA secondary structure. Firstly, we introduce the forest representation of RNA secondary structure. Next, we present a permutation-based genetic algorithm for predicting RNA secondary structures. We compute all possible helices of the RNA molecule and select the helices which have the highest similarity with the helices of a given homologous molecular structure. Finally, we select the structure which has the highest structure stability and similarity as predicted structure. Experiments have proved that the precise rates are improved.