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September 5, 2025

Prediction of circRNA-Disease Associations Based on Graph Isomorphism Networks and Graph Sampling Aggregation

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Authors

PLPengli LuXLXusheng LiuFGFentang Gao

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Overview

Computational framework predicts circRNA-disease links, demonstrating superior feature extraction in health models.

Key Points

  • GINSACDA achieved higher prediction performance compared to five existing models, indicating its effectiveness.
  • Five-fold cross-validation results on two datasets confirmed the model's ability to predict circRNA-disease associations.
  • This study uses graph isomorphism networks for feature extraction critical to predicting disease relationships.
  • Case studies of hepatocellular carcinoma and breast cancer further validate the model's predictive capabilities.

Cite This Study

Lu et al. (2025) studied this question.

synapsesocial.com/papers/68bb5f7a6d6d5674bcd03a61https://doi.org/10.1109/tcbbio.2025.3605047
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1RDGAN: Prediction of circRNA-Disease Associations Via Resistance Distance and Graph Attention Network2024 · 4 citations
  2. 2LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features2024 · 19 citations
  3. 3Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning2024 · 1 citations
  4. 4A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction2025
  5. 5AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA–drug sensitivity association prediction2026