An approach for RNA secondary structure prediction based on Bayesian network

Abstract

RNA secondary structure prediction is a fundamental problem in bioinformatics. This paper proposes a new approach to predict RNA secondary structure based on Bayesian network. Compared to the existing sophisticated prediction approaches such as Zuker's algorithm and the stochastic context-free grammar (SCFG) model, Bayesian network can naturally incorporate a priori knowledge from different models sources, and moreover, they have great expression capabilities. Our approach provides an effective method of combining free energy information of Zuker algorithm with statistical information from SCFG probability model. Basically, the proposed approach is suitable to all kinds of existing SCFG grammar models. Taking the BJK grammar model as an example, this paper gives a complete description of our prediction algorithm. When performing on RNA datasets with known structures, the experimental results show that the prediction accuracy is considerably improved. The sensitivity and the correlation coefficient are increased by 7.91% and 5.70%, respectively, compared to the SCFG approach alone.