ArticleCells2019
Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder.
Article in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Predicting human miRNA disease association with minimize matrix nuclear norm.Scientific reports · 2024Article
- HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction.Briefings in bioinformatics · 2024Article
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma.Frontiers in immunology · 2024Article
- MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.Molecules (Basel, Switzerland) · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder.Scientific reports · 2023Article
- An efficient model for predicting human diseases through miRNA based on multiple-types of contrastive learning.Frontiers in microbiology · 2023Article
- Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model.Frontiers in physiology · 2023Article
- microRNA Bioinformatics.Cells · 2022Article
- SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation.Biology · 2022Article
- m6A regulator-mediated RNA methylation modification patterns are involved in immune microenvironment regulation of coronary heart disease.Frontiers in cardiovascular medicine · 2022Article
- A Whole Transcriptome Analysis in Peripheral Blood Suggests That Energy Metabolism and Inflammation Are Involved in Major Depressive Disorder.Frontiers in psychiatry · 2022Article
- Identification of Serum Exosome-Derived circRNA-miRNA-TF-mRNA Regulatory Network in Postmenopausal Osteoporosis Using Bioinformatics Analysis and Validation in Peripheral Blood-Derived Mononuclear Cells.Frontiers in endocrinology · 2022Article
- Microarray and bioinformatic analysis reveal the parental genes of m6A modified circRNAs as novel prognostic signatures in colorectal cancer.Frontiers in oncology · 2022Article
- GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.PLoS computational biology · 2021Article
- EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network.BMC medical informatics and decision making · 2021Article
- SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.BMC bioinformatics · 2021Article
- PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction.Frontiers in genetics · 2021Article
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3 authors.
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Abstract
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.
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