Capsule Network for Predicting RNA-Protein Binding Preferences Using Hybrid Feature.

Abstract

RNA-Protein binding is involved in many different biological processes. With the progress of technology, more and more data are available for research. Based on these data, many prediction methods have been proposed to predict RNA-Protein binding preference. Some of these methods use only RNA sequence features for prediction, and some methods use multiple features for prediction. But, the performance of these methods is not satisfactory. In this study, we propose an improved capsule network to predict RNA-protein binding preferences, which can use both RNA sequence features and structure features. Experimental results show that our proposed method iCapsule performs better than three baseline methods in this field. We used both RNA sequence features and structure features in the model, so we tested the effect of primary capsule layer changes on model performance. In addition, we also studied the impact of model structure on model performance by performing our proposed method with different number of convolution layers and different kernel sizes.