ArticlePLoS computational biology2020
GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- Inferring circRNA-Disease Associations via Sparse Topological Representation Learning and Dual-View Decoding.International journal of molecular sciences · 2026Article
- The circular RNA landscape: Biogenesis, functions, identification pipelines, and biomedical applications.Non-coding RNA research · 2026Review
- Disentangling direct and pleiotropic SNP effects in alfalfa (Medicago sativa L.) using causal graph learning.Scientific reports · 2026Article
- THGC_MDA: a method for predicting the associations between mFrontiers in genetics · 2026Article
- Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs.ACS omega · 2025Article
- Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.Interdisciplinary sciences, computational life sciences · 2025Article
- GCNMF-SDA: predicting snoRNA-disease associations based on graph convolution and non-negative matrix factorization.Briefings in bioinformatics · 2025Article
- PanGIA: A universal framework for identifying association between ncRNAs and diseases.GigaScience · 2025Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.Briefings in bioinformatics · 2024Article
- Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets.Sensors (Basel, Switzerland) · 2024Review
- DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.Bioinformatics (Oxford, England) · 2024Article
- Identification of circRNA-disease associations via multi-model fusion and ensemble learning.Journal of cellular and molecular medicine · 2024Review
- IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.Briefings in bioinformatics · 2024Article
- DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.Interdisciplinary sciences, computational life sciences · 2024Article
- Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network.Plant methods · 2024Article
- A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.BMC biology · 2024Article
- iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.PLoS computational biology · 2023Article
- MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.Bioinformatics (Oxford, England) · 2023Article
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5 authors.
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Abstract
Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, we conducted case study experiments on diseases including breast cancer, glioma and colorectal cancer. The results showed that 16, 15 and 17 of the top 20 candidate circRNAs with the highest prediction scores were respectively confirmed by relevant literature and databases. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments.
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