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Article

Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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Author to whom correspondence should be addressed.
Cells 2019, 8(9), 1040; https://doi.org/10.3390/cells8091040
Received: 28 July 2019 / Revised: 31 August 2019 / Accepted: 2 September 2019 / Published: 6 September 2019
(This article belongs to the Special Issue microRNA Bioinformatics)
The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature. View Full-Text
Keywords: miRNA; disease; association prediction; variational autoencoder; generative model miRNA; disease; association prediction; variational autoencoder; generative model
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MDPI and ACS Style

Zhang, L.; Chen, X.; Yin, J. Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder. Cells 2019, 8, 1040. https://doi.org/10.3390/cells8091040

AMA Style

Zhang L, Chen X, Yin J. Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder. Cells. 2019; 8(9):1040. https://doi.org/10.3390/cells8091040

Chicago/Turabian Style

Zhang, Li, Xing Chen, and Jun Yin. 2019. "Prediction of Potential miRNA–Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder" Cells 8, no. 9: 1040. https://doi.org/10.3390/cells8091040

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