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July 1, 2026BMC BiologyOpen Access

AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA–drug sensitivity association prediction

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Authors

CCChao CaoMLMengli LiMGMaozu Guo

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Overview

Randomized trial demonstrates improved circRNA–drug sensitivity prediction, indicating advancements in precision medicine.

Key Points

  • The aim is to accurately predict circRNA–drug sensitivity associations to enhance understanding of therapeutic mechanisms.
  • Developed a graph representation learning framework for circRNA–drug sensitivity prediction.
  • Integrated semantic feature encoding and structural representation learning using graph convolutional networks.
  • Utilized cross-modal collaborative feature mining and large-scale heterogeneous graph for multi-source representation optimization.
  • Achieved superior performance compared to state-of-the-art methods across various validation techniques.
  • Demonstrated effectiveness through independent test evaluations and ablation studies.
  • Showed significant advancements in predicting therapeutic associations and facilitating drug response analysis.

Cite This Study

Cao et al. (2026) studied this question.

synapsesocial.com/papers/6a44ae8c5cd2549c8bc43bebhttps://doi.org/10.1186/s12915-026-02656-x
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Also Consider

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

  1. 1CFGSCDSA: Predicting circRNA-drug sensitivity associations based on collaborative feature learning and graph structure learning2026 · 1 citations
  2. 2DPMGCDA: Deciphering circRNA–Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder2024 · 12 citations
  3. 3<scp>Predicting</scp> the potential associations between <scp>circRNA</scp> and drug sensitivity using a multisource feature‐based approach2024 · 18 citations
  4. 4DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity2025
  5. 5DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity2025