ArticleCancers2021
GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- CSDPCDA: A miRNA-Mediated Cross-Semantic Regulatory Network Framework for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2026Article
- THGC_MDA: a method for predicting the associations between mFrontiers in genetics · 2026Article
- Novel graph-based centralized and decentralized approaches for early AKI prediction.Scientific reports · 2025Article
- MOPSOGAT: Predicting CircRNA-Disease Associations via Improved Multi-objective Particle Swarm Optimization and Graph Attention Network.Interdisciplinary sciences, computational life sciences · 2025Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- Identification of circRNA-disease associations via multi-model fusion and ensemble learning.Journal of cellular and molecular medicine · 2024Review
- 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
- Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest.Frontiers in microbiology · 2024Article
- HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.BMC bioinformatics · 2023Article
- HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.BMC bioinformatics · 2023Article
- MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.Bioinformatics (Oxford, England) · 2023Article
- iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.PLoS computational biology · 2023Article
- Fragment-pair based drug molecule solubility prediction through attention mechanism.Frontiers in pharmacology · 2023Article
- Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.Biomolecules · 2022Article
- Predicting miRNA-disease associations based on graph attention network with multi-source information.BMC bioinformatics · 2022Article
- Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data.Frontiers in genetics · 2022Article
- Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.Frontiers in microbiology · 2022Article
- Identifying lncRNA-disease association based on GAT multiple-operator aggregation and inductive matrix completion.Frontiers in genetics · 2022Article
- GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2021Article
- CircIMPACT: An R Package to Explore Circular RNA Impact on Gene Expression and Pathways.Genes · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA-disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA-miRNA-mRNA axis plays an important role in the generation and development of diseases, circRNA-miRNA interactions and disease-mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA-disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.
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What Socratic holds
Registered trials
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 Socratic graph.