To predict RNA secondary structures, traditional stochastic grammar models need to collect plenty of related RNA sequences, which limits the practical application of this method. In order to use a large number of unlabeled RNA sequences effectively for structure prediction, the Semi-supervised method has been applied to stochastic grammar models. We use a small amount of labeled RNA samples and a large number of unlabeled samples as a training set of prediction model. Designing a semi-supervised learning model based on EM algorithm, using a SCFG model based on generative method as classifier, ...
