Deep multi-scale attention network for RNA-binding proteins prediction

Abstract RNA-binding proteins (RBPs) play a significant part in several biological processes in the living cell, such as gene regulation and mRNA localization. The research indicates that the mutation of RBPs will lead to some serious diseases. Several deep learning methods, especially the model based on convolutional neural network (CNN), have been used to predict the binding sites. However, these methods only use single-scale filters to extract a fixed length of motifs features, which restricts the performance of prediction. For the sequence data, different sizes of filters may learn different biological information of the RNA sequence. Therefore, a deep multi-scale attention network (DeepMSA) based on convolutional neural network is proposed to predict the sequence-binding preferences of RBPs. DeepMSA extracts features by multi-scale CNNs and integrates these features with an attention model to predict the RBPs and binding motifs. Experiments demonstrate DeepMSA outperforms several state-of-the-art methods on the in-vivo and in-vitro datasets. The results indicate that attention can make the model learn the consistent pattern of candidate motifs, which can provide some important guiding advice for RBP motifs.