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

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外文摘要：Predicting RNA binding protein(RBP)binding sites on circular RNAs(circRNAs)is a fundamental step to understand their interaction mechanism.Numerous computational methods are developed to solve this prob-lem,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 matri-ces.We preprocess them using CNN before fusion.To capture the feature dependencies,we utilize temporal convolu-tional 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 im-provement of circ-CNNED.

外文关键词：

Circular RNAs(circRNAs)RNA binding proteinsConvolutional neural networkTemporal con-volutional networkEncoder-decoder network

作者：

Yajing GUO、Xiujuan LEI、Yi PAN

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作者单位：

School of Computer Science,Shaanxi Normal University,Xi'an 710119,China

Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China

Department of Computer Science,Georgia State University,Atlanta,GA 30302,USA

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesShenzhen Key Laboratory of Intelligent Bioinformatics

项目编号：

6227228861972451U22A2041ZDSYS20220422103800001

出版年：

2024

DOI：

10.23919/cje.2022.00.361

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(1)

参考文献量37