rBPDL:Predicting RNA-Binding Proteins Using Deep Learning

RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimental methods have made great progress in predicting RBP, they are time-consuming and not flexible. Therefore, we developed a network model, rBPDL, by combining a convolutional neural network and long short-term memory for multilabel classification of RBPs. Moreover, to achieve better prediction results, we used a voting algorithm for ensemble learning of the model. We compared rBPDL with state-of-the-art methods and found that rBPDL significantly improved identification performance for the RBP68 dataset, with a macro-Area Under Curve (AUC), micro-AUC, and weighted AUC of 0. 936, 0. 962, and 0. 946, respectively. Furthermore, through AUC statistical analysis of the RBP domain, we analyzed the performance of rBPDL and found that the RBP identification performance in the same domain was similar. In addition, we analyzed the performance preferences and physicochemical properties of the binding protein amino acids and explored the characteristics that affect the binding by using the RBP86 dataset.

Authors

• Mengting Niu
• Jin Wu
• Quan Zou
• Zhendong Liu School of Information and Communication Engineering, University of Electronic Science and Technology of China
• Lei Xu Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, Shanghai, China

Keywords

• Proteins
• RNA
• Training
• Encoding
• Predictive models
• Feature extraction
• Deep learning
• RNA-binding Proteins
• Convolutional Neural Network
• Short-term Memory
• Long Short-term Memory
• Multi-label
• Role In Cell Development
• Physicochemical Properties Of Amino Acids
• Machine Learning
• Activation Function
• Effects In Models
• Learning Rate
• Support Vector Machine
• Running Time
• Convolutional Layers
• Time Cost
• RNA Binding
• Network Performance
• Ensemble Model
• Rectified Linear Unit Function
• Residue Interaction
• Batch Normalization Layer
• Conv Layer
• One-hot Encoding
• Long Short-term Memory Model
• Position Weight Matrices
• Model In This Paper
• Ensemble Results
• Recurrent Neural Network
• RNA-binding protein
• multilabel classification
• Binding Sites
• Neural Networks, Computer
• Protein Binding