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Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD_MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD_MVMF. These evaluation metrics verify that PCD_MVMF has better prediction performance than other methods. To further illustrate the performance of PCD_MVMF, case studies of common diseases are conducted. Therefore, PCD_MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.


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CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization

Show Author's information Yuchen ZhangXiujuan Lei( )Zengqiang FangYi Pan( )
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA

Abstract

Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD_MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD_MVMF. These evaluation metrics verify that PCD_MVMF has better prediction performance than other methods. To further illustrate the performance of PCD_MVMF, case studies of common diseases are conducted. Therefore, PCD_MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.

Keywords:

circular RNAs (circRNAs), circRNA-disease associations, matepath2vec++, matrix factorization
Received: 02 June 2020 Revised: 09 October 2020 Accepted: 10 October 2020 Published: 16 November 2020 Issue date: December 2020
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Publication history

Received: 02 June 2020
Revised: 09 October 2020
Accepted: 10 October 2020
Published: 16 November 2020
Issue date: December 2020

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© The authors 2020

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61972451, 61672334, and 61902230) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (Nos.GK201901010 and 2018TS079).

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