ArticleBiology2022
SGCNCMI: A New Model Combining Multi-Modal Information to Predict circRNA-Related miRNAs, Diseases and Genes.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 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
- Article
- iHofman: a predictive model integrating high-order and low-order features with weighted attention mechanisms for circRNA-miRNA interactions.BMC biology · 2025Article
- Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.BMC biology · 2025Article
- DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.BMC biology · 2025Article
- CMAGN: circRNA-miRNA association prediction based on graph attention auto-encoder and network consistency projection.BMC bioinformatics · 2024Article
- Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.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
- Biolinguistic graph fusion model for circRNA-miRNA association prediction.Briefings in bioinformatics · 2024Article
- Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.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
- KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.iScience · 2023Article
- LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model.Frontiers in genetics · 2023Article
- Identifying circRNA-miRNA interaction based on multi-biological interaction fusion.Frontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational prediction of miRNAs, diseases, and genes associated with circRNAs has important implications for circRNA research, as well as provides a reference for wet experiments to save costs and time. In this study, SGCNCMI, a computational model combining multimodal information and graph convolutional neural networks, combines node similarity to form node information and then predicts associated nodes using GCN with a distributive contribution mechanism. The model can be used not only to predict the molecular level of circRNA-miRNA interactions but also to predict circRNA-cancer and circRNA-gene associations. The AUCs of circRNA-miRNA, circRNA-disease, and circRNA-gene associations in the five-fold cross-validation experiment of SGCNCMI is 89.42%, 84.18%, and 82.44%, respectively. SGCNCMI is one of the few models in this field and achieved the best results. In addition, in our case study, six of the top ten relationship pairs with the highest prediction scores were verified in PubMed.
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