DeepSloop: A Recurrent Neural Network Learns Complex Rules to Detect StemLoopForming RNA Sequences The analysis of RNA structure is an important problem because RNA plays essential roles throughout all living things. Increasingly, RNA plays pivotal roles in biotechnology including RNA silencing, guide RNAs for CRISPR, ligandbinding aptamers, and drug design. While several studies have used deep learning models for analyzing RNA structure, these approaches rely on the outputs of traditional dynamic programming algorithms based on known thermodynamic energy parameters during training. The challenge remains to train neural networks to independently learn thermodynamic principles, so that potentially new biology can be uncovered. Here we present DeepSloop, a deep recurrent network capable of distinguishing hairpinforming sequences from nonhairpinforming, and that is trained purely on classlabeled nucleotide sequences. We demonstrate that DeepSloop can accurately classify hairpin stemloops, and thus learned a distinct internal representation of structure that includes destabilizing energies of bulges and internal loops.

A deep neural network approach using distributed representations of RNA sequence and structure for identifying binding site of RNAbinding proteins RNAbinding proteins (RBPs) play a crucial role in the posttranscriptional regulation of RNAs. Identification of RBP binding sites is a key step to understand the biological mechanism of posttranscriptional regulation. Although many computational methods have been developed for predicting RNAprotein binding sites, few study considers the kmer embedding representation of RNA primary sequence and secondary structure specificities. In this paper, we develop a general deep learning framework, named deepRKE, to predict RNAprotein binding sites. deepRKE takes an unsupervised shallow twolayer neural network to automatically learn the distributed representation of kmers by taking their neighbor context into account. Compared to conventional kmers approach, distributed representations effectively detect the latent relationship and similarity between kmers. The distributed representations of the sequences and secondary structures are fed into CNN convolutional neural network (CNN) and a bidirectional long short term memory network (BLSTM) to discriminate the RBP binding sites from unbound sites. We comprehensively evaluate deepRKE on two largescale RBP binding sites datasets, and the experimental results show that deepRKE achieves better performance than five competitive methods.