SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stack...

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Bibliographic Details
Main Authors: Shudong Wang, Boyang Lin, Yuanyuan Zhang, Sibo Qiao, Fuyu Wang, Wenhao Wu, Chuanru Ren
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Cells
Subjects:
miRNA
disease
association prediction
stacked graph autoencoder
higher-order features
Online Access:https://www.mdpi.com/2073-4409/11/24/3984

Internet

https://www.mdpi.com/2073-4409/11/24/3984

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