# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 1 Introduction  
RNA plays a critical role as a crucial carrier for genetic information [1, 2]. RNA-binding proteins (RBPs) are essential regulators of various stages of cellular RNA, including RNA transcription, RNA translation, RNA editing, and mRNA localization [3, 4]. An RBP contains at least one RNA-binding domain (RBD) [5]. It can recognize specific binding sites on RNA and form polymers with RNA, making it possible to regulate the expression of RNA functions [6, 7, 8]. More than 2000 RBPs have been identified [9], but only a few RBP processes are thoroughly understood. Mutations in genes, RNA function failure, and other issues are directly linked to aberrant combinations of RBP and RNA, which biological studies have shown to be a significant contributor to the development of many diseases. For example, IRP1 binding to FTL mRNA is disrupted by mutations in the iron-responsive region of the FTL gene, leading to hyperferritinemia-cataract syndrome [10, 11]. Therefore, solving more complex biological problems requires a thorough understanding of RBP-binding sites and preferences. It is crucial to investigate the binding specificity between RBP and RNA. With the advent of high-throughput sequencing technology, biological experiments have confirmed the binding RNAs and the binding sites of various RBPs [12, 13]. Crosslinking and immunoprecipitation sequencing (CLIP-seq) [14] and RNA immunoprecipitationsequencing (RIP-seq) [15] have been widely applied to RBP binding sequencing problems. However, biological sequencing techniques are costly and time-consuming. Developing computational methods to accurately predict binding sites and preferences utilizing existing data is crucial. It has been shown that the selection of RBP binding sites is related to both sequence and secondary structure [16; 17; 18; 19]. The secondary structure of RNA is crucial to scientific research and consists of six substructures. The hierarchical information from RNA secondary structure has been shown in biological experiments to affect binding sites [20; 21; 22]. Various machine-learning methods have been applied to predict RBP-RNA binding sites as computational methods have advanced. RNAcontext [23] was used to predict binding strength by calculating the position weight matrix of binding motifs. RCK [24] was used to introduce RNA secondary structure, based on RNAcontext, to capture local preferences more accurately. GraphProt, proposed in [25], was used to encode each RNA sequence as a hypergraph containing sequence and secondary structure information. The prediction accuracy could be significantly improved by training the model with a machine-learning algorithm. However, all the machine-learning algorithms mentioned above require manual feature extraction and specific prior knowledge. The development of high-throughput sequencing technology has resulted in large amounts of data. The implementation of deep-learning algorithms in this field offers the opportunity to generate fully data-driven predictions of binding sites. Deep-learning methods can automatically extract features from data, compensating for the limitations of manual feature extraction in machine learning. DeepBind [26], proposed in 2015, was the first deep-learning algorithm applied to this problem. The authors used a convolutional neural network (CNN) to automate the extraction of binding motifs from RNA sequences fully. iDeepE [27] could learn the features of both long and short sequences using a CNN and combined the results to predict binding sites. According to biological experiments, the RNA secondary structure, which is a graph structure, strongly correlates with the choice of binding site. Based on DeepBind, iDeepS [28] added bi-directional long short-term memory (Bi-LSTM) and the RNA secondary structure. A CNN was used to extract sequence features, and the Bi-LSTM extracted long-term dependence between the sequences and secondary structures. The developers of DeepRPK [29] used a word-embedding algorithm to extract the features of the RNA sequence and secondary structure.