ArticleScientific reports2020
Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine.PLoS computational biology · 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
- MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks.PLoS computational biology · 2025Article
- Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor.Biomolecules · 2025Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- Directed Circularization of a Short RNA.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion.Scientific reports · 2023Article
- MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network.BMC bioinformatics · 2022Article
- circGPA: circRNA functional annotation based on probability-generating functions.BMC bioinformatics · 2022Article
- Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.Biomolecules · 2022Article
- Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data.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
- Double matrix completion for circRNA-disease association prediction.BMC bioinformatics · 2021Article
- Predicting LncRNA-Disease Association by a Random Walk With Restart on Multiplex and Heterogeneous Networks.Frontiers in genetics · 2021Article
- iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.Molecular genetics and genomics : MGG · 2021Article
- Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.Molecular diagnosis & therapy · 2021Article
- Prioritizing CircRNA-Disease Associations With Convolutional Neural Network Based on Multiple Similarity Feature Fusion.Frontiers in genetics · 2020Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
CircRNA is a special type of non-coding RNA, which is closely related to the occurrence and development of many complex human diseases. However, it is time-consuming and expensive to determine the circRNA-disease associations through experimental methods. Therefore, based on the existing databases, we propose a method named RWRKNN, which integrates the random walk with restart (RWR) and k-nearest neighbors (KNN) to predict the associations between circRNAs and diseases. Specifically, we apply RWR algorithm on weighting features with global network topology information, and employ KNN to classify based on features. Finally, the prediction scores of each circRNA-disease pair are obtained. As demonstrated by leave-one-out, 5-fold cross-validation and 10-fold cross-validation, RWRKNN achieves AUC values of 0.9297, 0.9333 and 0.9261, respectively. And case studies show that the circRNA-disease associations predicted by RWRKNN can be successfully demonstrated. In conclusion, RWRKNN is a useful method for predicting circRNA-disease associations.
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