Circular RNAs (circRNAs), with their crucial roles in gene regulation and disease development, have become a rising star in the RNA world. A lot of previous wet-lab studies focused on the interaction mechanisms between circRNAs and RNA-binding proteins (RBPs), as the knowledge of circRNA-RBP association is very important for understanding functions of circRNAs. Recently, the abundant CLIP-Seq experimental data has made the large-scale identification and analysis of circRNA-RBP interactions possible, while no computational tool based on machine learning has been developed yet. We present a new deep learning-based method, CRIP (CircRNAs Interact with Proteins), for the prediction of RBP binding sites on circRNAs, using only the RNA sequences. In order to fully exploit the sequence information, we propose a stacked codon-based encoding scheme and a hybrid deep learning architecture, in which a convolutional neural network (CNN) learns high-level abstract features and a recurrent neural network (RNN) learns long dependency in the sequences. We construct 37 datasets including sequence fragments of binding sites on circRNAs, and each set corresponds to one RBP. The experimental results show that the new encoding scheme is superior to the existing feature representation methods for RNA sequences, and the hybrid network outperforms conventional classifiers by a large margin, where both the CNN and RNN components contribute to the performance improvement. %Compared to the existing predictors for linear RNAs, CRIP yields a better performance, with an average AUC of 87.2%. To the best of our knowledge, CRIP is the first machine learning-based tool specialized in the prediction of circRNA-RBP interactions, which is expected to play an important role for large-scale function analysis of circRNAs.
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