## 2 Related Works  
In the past decade, several computational methods have been proposed to predict RBPs. According to their feature representations to proteins, these methods can be roughly split into two categories, i.e., sequence-based and structure-based methods. Due to fact that the available structure data of proteins are relatively small, structure-based methods cannot be widely applied and have lacked attractiveness recently. To the best of our knowledge, the most recent work was the BindUP [9] proposed in 2016. It was a web server for the non-homology-based prediction of RBPs. BindUP extracted features, such as electrostatic surface patches, the molecular weight, surface accessibility and the moment dipole of the protein chain, from a 3D structure of a protein or a structural model. These numeric features were then fed to the supervised learning framework to train a support vector machine (SVM) classifier with a linear kernel function. In contrast, sequence-based methods are receiving more and more attention. Sequence-based methods derive various features from protein primary sequences, such as evolutionary conservation information, physicochemical properties or predicted structure information. Based on these features, they train classifiers to distinguish RBPs from non-RBPs via supervised learning. Position-specific scoring matrixes (PSSMs) and physicochemical properties are two kinds of features that are frequently utilized to predict RBPs. [PERSON] et al. [10] trained an SVM classifier denoted as RNAPd based on the PSSMs of proteins. RNAPred firstly identifies RBPs according to the number of predicted binding residues. If the number exceeds the threshold for RBPs, the testing sample will be classified as an RBP. And if the number is less than the threshold for non-RBPs, the testing sample will be classified as a non-RBP. In other cases, the trained SVM is responsible for further prediction. The catRAPID [11] signature computed the Pearson correlation coefficient between physicochemical properties and annotated RNA-binding domains (RBDs) and trained an SVM with an RBF kernel based on the fraction of residues with a high correlation and associated RBDs. Similar to catRAPID, [PERSON] et al. [12] proposed the APRICOT method that tried to predict RBDs from protein sequences. They described RBD characteristics using PSSMs, instead of physicochemical properties. [PERSON] and [PERSON] integrated physicochemical properties and PSSMs to train an SVM model named RBPPred [13]. They demonstrated a performance improvement for the integrated features over any single feature. Besides PSSMs and physicochemical properties, the structure features predicted from sequences are also used to identify RBPs. For example, SPOT-Seq-RNA [14] combined template-based structure-prediction software with binding-affinity prediction software for protein-RNA complexes. Recently, [PERSON] et al. [15] introduced a novel sequence-derived feature to train an SVM model, TriPepSVM, to predict RBPs. They computed frequencies of tripeptide motifs by continuously moving a sliding window in a protein sequence. On the dataset of three species, experimental results demonstrated that this feature was computationally efficient and discriminative for identifying RBPs. With the wide application of deep learning in bioinformatics fields [16; 17; 18; 19; 20], several works tried to improve the performance of RPB predictors by resorting to deep learning. Deep-RBPPred [21] combined five physicochemical properties using a global composition feature encoding method (CTD) to form a 160-dimension feature vector and then train a convolutional neural network (CNN) with the reshaped \(8\times 20\) feature tensor as input. Furthermore, DeepMVF-RBP [22] encoded physicochemical properties using three different methods, i.e., CTD, conjoint triad (CT) and parallel correlation pseudo amino acid composition (PC-PseAAC) and then concatenated them together to train a deep belief network (DBN).