ArticlePLoS computational biology2020
iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.
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 22 papers.
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Who cites it
22 citing papers in PubMed, 88 citations in OpenAlex.
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations.Scientific reports · 2026Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.Briefings in bioinformatics · 2024Article
- Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.BMC medical informatics and decision making · 2024Article
- Identification of circRNA-disease associations via multi-model fusion and ensemble learning.Journal of cellular and molecular medicine · 2024Review
- DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder.Interdisciplinary sciences, computational life sciences · 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
- BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.Frontiers in genetics · 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
- 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
- Promising Roles of Circular RNAs as Biomarkers and Targets for Potential Diagnosis and Therapy of Tuberculosis.Biomolecules · 2022Review
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 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
- Chaos game representation and its applications in bioinformatics.Computational and structural biotechnology journal · 2021Review
- iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.Molecular genetics and genomics : MGG · 2021Article
- DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation.Journal of translational medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.
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Registered trials
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