A Review About RNA–Protein-Binding Sites Prediction Based on Deep Learning

RNA-binding proteins (RBPs) play crucial roles in gene regulation. The advent of high-throughput experimental methods, has generated a huge volume of experimentally verified binding sites of RNA-binding proteins and greatly advanced the genome-wide studies of RNA-protein interactions. Many computational approaches have been proposed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we discuss machine learning and deep learning approaches, mainly focusing on the prediction of RNA and proteins binding sites on RNAs by deep learning. Furthermore, we discuss the advantages and disadvantages of these approaches. The workflow of deep learning is also revealed. We recommend some promising future directions of deep learning models in the study of RBP-binding sites on RNAs, especially the embedding, generative adversarial net, and attention model. Extraction and visualization methods involving motif are illustrated. Finally, we summarize the previous studies, and then compare the performance on different dataset.