Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network

In this study, we present a deep learning based approach iDeepV. It first applies an unsupervised shallow two-layer neural network to automatically learn the distributed representation of k-mers by considering their neighbor context. Compared to the conventional k-mers approach, the new distributed representation captures the latent relationship of k-mers, in which the similarity between k-mers is taken into consideration. Then, the learned distributed representations of the input sequences are used as inputs for a convolutional neural network (CNN) to discriminate the RBP bound sites from the unbound sites. We comprehensively evaluate the iDeepV on two large-scale RBP binding sites datasets. The results show that iDeepV can yield comparable performance than the state-of-the-art methods. The iDeepV algorithm is available at https://github.com/xypan1232/iDeepV.