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

GUO Yajing; LEI Xiujuan; PAN Yi

doi:10.23919/cje.2022.00.361

Volume 33 Issue 1

Jan. 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(1): 256-263

Yajing GUO, Xiujuan LEI, Yi PAN, “An Encoding-Decoding Framework Based on CNN for circRNA-RBP Binding Sites Prediction,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 256–263, 2024 doi: 10.23919/cje.2022.00.361

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Yajing GUO, Xiujuan LEI, Yi PAN, “An Encoding-Decoding Framework Based on CNN for circRNA-RBP Binding Sites Prediction,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 256–263, 2024 doi: 10.23919/cje.2022.00.361

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

doi: 10.23919/cje.2022.00.361

GUO Yajing^1
,,
LEI Xiujuan^{1
,
,},
PAN Yi^{2, 3
,}

1.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2.
Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3.
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA

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Author Bio:
Yajing GUO is currently pursuing the Ph.D. degree in the School of Computer Science at Shaanxi Normal University, Xi’an, China. Her research interests include bioinformatics and deep learning. (Email: guoyajing@snnu.edu.cn)

Xiujuan LEI is a Professor in the School of Computer Science at Shaanxi Normal University, Xi’an, China. Her research interests include intelligent computing and bioinformatics. (Email: xjlei@snnu.edu.cn)

Yi PAN is currently a Professor of the Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China. He has served as Chair of Computer Science Department at Georgia State University during 2005–2020. His current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. (Email:yi.pan@siat.ac.cn)
Corresponding author: Email: xjlei@snnu.edu.cn
Received Date: 2022-11-01
Accepted Date: 2023-04-17

Available Online: 2023-08-08

Publish Date: 2024-01-05

Abstract

Abstract

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 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.
- Circular RNAs (circRNAs),
- RNA binding proteins,
- Convolutional neural network,
- Temporal convolutional network,
- Encoder-decoder network

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References(37)

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