circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

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

作者机构：School of Computer ScienceShaanxi Normal UniversityXi’an 710119China Faculty of Computer Science and Control EngineeringShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2023年第17卷第5期

页      面：217-225页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 0703[理学-化学] 100214[医学-肿瘤学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61972451,61902230) the Fundamental Research Funds for the Central Universities,Shaanxi Normal University(GK202103091) 

主　　题：circRNAs RBPs CNN BiLSTM self-attention mechanism 

摘      要：Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature *** view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding *** have constructed a data set which includes eight ***,circ2CBA encodes circRNA sequences using the one-hot ***,a two-layer convolutional neural network(CNN)is used to initially extract the *** CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the *** AUC value of circ2CBA reaches *** of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.