期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：2
出版社：IJCSI Press
摘要：The identification of RNA secondary structures has been among the most exciting recent developments in biology and medical science. Prediction of RNA secondary structure is a fundamental problem in computational structural biology. For several decades, free energy minimization has been the most popular method for prediction from a single sequence. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally -measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. This paper proposes a new algorithm that computes base pairing pattern for RNA molecule. Complex internal structures in RNA are fully taken into account. It supports the calculation of stochastic context-free grammars (SCFGs), and equilibrium concentrations of duplex structures. This new algorithm is compared with dynamic programming benchmark mfold and algorithms (Tfold, and MaxExpect). The results showed that the proposed algorithm achieved better performance with respect to sensitivity and positive predictive value.
