Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders

GUO Yanbu; MA Huan; LI Chaoyang; ZHOU Dongming

doi:10.11999/JEIT220354

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1786-1794

GUO Yanbu, MA Huan, LI Chaoyang, ZHOU Dongming. Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1786-1794. doi: 10.11999/JEIT220354

Citation:

GUO Yanbu, MA Huan, LI Chaoyang, ZHOU Dongming. Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1786-1794. doi: 10.11999/JEIT220354

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Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders

doi: 10.11999/JEIT220354

1.
College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China

Funds: The National Natural Science Foundation of China (62066047), The Doctor Scientific Research Fund of Zhengzhou University of Light Industry (2021BSJJ032)

Received Date: 2022-03-31
Rev Recd Date: 2022-06-19

Available Online: 2022-06-24

Publish Date: 2023-05-10

Abstract

Abstract

micro RiboNucleic Acid (miRNA) plays an important role in the process of gene expression and transcription, and is closely related to the production of diseases. Biological experimental methods for disease miRNA association prediction are costly and time-consuming. To extract contextual information in heterogeneous networks of diseases and miRNAs, a high-performance Variational Gated AutoEncoder Network (VGAE-N) and a gated multi-layer perceptron are designed based on gated mechanisms and convolutions, and then a deep variational gated neural model is constructed for inferring disease miRNA associations. Multisource information first is integrated between miRNAs and diseases, and then the comprehensive similarity matrix is obtained between miRNAs and diseases. Based on a comprehensive network and the miRNA disease adjacency matrix, topological information is further extracted for miRNAs and diseases, respectively. Based on the miRNA disease adjacency matrix, the nonnegative matrix decomposition is used to extract low dimensional denoising features of miRNA and diseases. The experimental results show that the proposed model can effectively conduct miRNA disease association prediction, and provide reliable technical support for biological experiments.
- Variational autoencoder,
- Matrix factorization,
- Gated mechanisms,
- Micro (miRNA) disease associations

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References(34)

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