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AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association
Chao Cao1,2, Mengli Li2, Maozu Guo3
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
BMC Biology
|June 29, 2026
Summary
This study introduces a novel graph learning framework to predict circular RNA-drug sensitivity. The model effectively integrates diverse data, outperforming existing methods for precision medicine applications.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Circular RNAs (circRNAs) are key regulators in disease and drug response.
- Accurate circRNA-drug sensitivity prediction is vital for precision medicine.
- Existing methods struggle with integrating semantic and structural data.
Purpose of the Study:
- To develop an advanced computational framework for circRNA-drug sensitivity prediction.
- To overcome limitations of current methods in feature integration and optimization.
- To enhance understanding of therapeutic mechanisms and drug response.
Main Methods:
- An end-to-end graph representation learning framework.
- Joint modeling of homogeneous similarity and heterogeneous interactions.
- Integration of fused similarity graphs, attention-based semantic encoding, and graph convolutional networks.
- Cross-modal collaborative feature mining for multi-source representation optimization.
Main Results:
- The proposed framework demonstrates superior performance in circRNA-drug sensitivity prediction.
- Consistent results across 5-fold/10-fold cross-validation and independent tests.
- Validation through ablation and case studies confirms effectiveness.
Conclusions:
- The framework offers a robust computational strategy for predicting circRNA-drug sensitivity.
- Provides a valuable tool for identifying therapeutic associations.
- Facilitates advancements in drug response analysis and precision medicine.
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