RNA secondary structure prediction based on long-range interaction and Support Vector Machine

The structure of RNA is very important in biological processes. Over the recent years, lots of machine learning method have been emerged to predict the secondary structure of RNA. In this paper, we use Support Vector Machine to predict secondary structure of RNA sequence. Meanwhile, a sequence-based method is proposed by combining a new feature representation which is based on RNA long-range interaction. We first quote E-NSSEL labels to represent the secondary structure of RNA. Combining with the definition of a new feature vector based on long-range interaction, the secondary structure of test sequence is predicted by SVM model, and the corresponding E-NSSEL sequence is consequently obtained. This sequence can be restored to secondary structure finally. The results which are obtained from RNA training and testing datasets show that this long-range-sequence-based method is superior to those method without new feature. It has higher prediction accuracy as considering the new feature. Moreover, it can predict RNA sequences with long length, which is difficult to deal with traditional folding prediction. Furthermore, it suggests that our method may provide a reliable tool for RNA secondary structure prediction, including the prediction of RNA with pseudoknots.