An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

作     者：Yajing GUO Xiujuan LEI Yi PAN Yajing GUO;Xiujuan LEI;Yi PAN

作者机构：School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2024年第33卷第1期

页      面：256-263页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 0711[理学-系统科学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 62272288,61972451, and U22A2041) the Fundamental Research Funds for the Central Universities Shenzhen Key Laboratory of Intelligent Bioinformatics (Grant No.ZDSYS20220422103800001) 

主　　题：Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks 

摘      要：Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.