ArticleMolecular diagnosis & therapy2021
Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.
Article in Molecular diagnosis & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- Self-supervised learning on graphs predicts non-coding RNA and disease associations.Scientific reports · 2026Article
- MOPSOGAT: Predicting CircRNA-Disease Associations via Improved Multi-objective Particle Swarm Optimization and Graph Attention Network.Interdisciplinary sciences, computational life sciences · 2025Article
- Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs.ACS omega · 2025Article
- A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.Molecular diversity · 2025Article
- MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks.PLoS computational biology · 2025Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- A Model Transfer Method among Spectrometers Based on Improved Deep Autoencoder for Concentration Determination of Heavy Metal Ions by UV-Vis Spectra.Sensors (Basel, Switzerland) · 2023Article
- MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.BMC bioinformatics · 2022Article
- SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.Biology · 2022Article
- KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning.Frontiers in genetics · 2022Article
- GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions.Frontiers in genetics · 2022Article
- CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.BMC bioinformatics · 2021Article
- Circular RNAs and complex diseases: from experimental results to computational models.Briefings in bioinformatics · 2021Review
- LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution.International journal of molecular sciences · 2021Article
- GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2021Article
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2 authors.
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Abstract
aimCircular RNAs (circRNA) are endogenous non-coding RNA molecules with a stable circular conformation. Growing evidence from recent experiments reveals that dysregulations and abnormal expressions of circRNAs are correlated with complex diseases. Therefore, identifying the causal circRNAs behind diseases is invaluable in explaining the disease pathogenesis. Since biological experiments are difficult, slow-progressing, and prohibitively expensive, computational approaches are necessary for identifying the relationships between circRNAs and diseases.
methodsWe propose an ensemble method called AE-RF, based on a deep autoencoder and random forest classifier, to predict potential circRNA-disease associations. The method first integrates circRNA and disease similarities to construct features. The integrated features are sent to the deep autoencoder, to extract hidden biological patterns. With the extracted deep features, the random forest classifier is trained for association prediction. RESULTS AND DISCUSSION: AE-RF achieved AUC scores of 0.9486 and 0.9522, in fivefold and tenfold cross-validation experiments, respectively. We conducted case studies on the top-most predicted results and three common human cancers. We compared the method with state-of-the-art classifiers and related methods. The experimental results and case studies demonstrate the prediction power of the model, and it outperforms previous methods with high degree of robustness. Training the classifier with the unique features retrieved by the autoencoder enhanced the model's predictive performance. The top predicted circRNAs are promising candidates for further biological tests.
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