ArticleFrontiers in genetics2022
KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 45 citations in OpenAlex.
- The circular RNA landscape: Biogenesis, functions, identification pipelines, and biomedical applications.Non-coding RNA research · 2026Review
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- BCheM: a framework integrating molecular attributes and behavioral representations for precise prediction of drug resistance across multiple disease contexts.Frontiers in bioinformatics · 2026Article
- DAMFCMI: capturing cross-view interactions via hybrid attention for circRNA-MiRNA interaction prediction.Bioinformatics advances · 2026Article
- iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions.BMC biology · 2025Article
- DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.BMC biology · 2025Article
- MicroRNA Signatures: Illuminating Minimal Residual Disease Monitoring in Juvenile Myelomonocytic Leukemia - A Review.Journal of hematology · 2025Review
- GTMALoc: prediction of miRNA subcellular localization based on graph transformer and multi-head attention mechanism.Frontiers in genetics · 2025Article
- CMAGN: circRNA-miRNA association prediction based on graph attention auto-encoder and network consistency projection.BMC bioinformatics · 2024Article
- A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer.Briefings in bioinformatics · 2024Article
- A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers.Briefings in bioinformatics · 2024Article
- BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.BMC bioinformatics · 2024Article
- Circular RNAs in Cell Cycle Regulation of Cancers.International journal of molecular sciences · 2024Review
- Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
- Dysregulated Expression Patterns of Circular RNAs in Cancer: Uncovering Molecular Mechanisms and Biomarker Potential.Biomolecules · 2024Review
- Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network.Plant methods · 2024Article
- Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.Briefings in bioinformatics · 2024Article
- Biolinguistic graph fusion model for circRNA-miRNA association prediction.Briefings in bioinformatics · 2024Article
- Predicting circRNA-miRNA interactions utilizing transformer-based RNA sequential learning and high-order proximity preserved embedding.iScience · 2024Article
- BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emerging evidence has revealed that circular RNA (circRNA) is widely distributed in mammalian cells and functions as microRNA (miRNA) sponges involved in transcriptional and posttranscriptional regulation of gene expression. Recognizing the circRNA-miRNA interaction provides a new perspective for the detection and treatment of human complex diseases. Compared with the traditional biological experimental methods used to predict the association of molecules, which are limited to the small-scale and are time-consuming and laborious, computing models can provide a basis for biological experiments at low cost. Considering that the proposed calculation model is limited, it is necessary to develop an effective computational method to predict the circRNA-miRNA interaction. This study thus proposed a novel computing method, named KGDCMI, to predict the interactions between circRNA and miRNA based on multi-source information extraction and fusion. The KGDCMI obtains RNA attribute information from sequence and similarity, capturing the behavior information in RNA association through a graph-embedding algorithm. Then, the obtained feature vector is extracted further by principal component analysis and sent to the deep neural network for information fusion and prediction. At last, KGDCMI obtains the prediction accuracy (area under the curve [AUC] = 89.30% and area under the precision-recall curve [AUPR] = 87.67%). Meanwhile, with the same dataset, KGDCMI is 2.37% and 3.08%, respectively, higher than the only existing model, and we conducted three groups of comparative experiments, obtaining the best classification strategy, feature extraction parameters, and dimensions. In addition, in the performed case study, 7 of the top 10 interaction pairs were confirmed in PubMed. These results suggest that KGDCMI is a feasible and useful method to predict the circRNA-miRNA interaction and can act as a reliable candidate for related RNA biological experiments.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.