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This is a Validation Study study.

November 30, 2025Frontiers in GeneticsOpen Access

DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity

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Analysis shows improved drug response prediction in drug-sensitive models, indicating a novel framework for circRNA-drug interactions.

DMAGCL achieves state-of-the-art performance with an average AUC of 0.8940 across five-fold cross-validation, highlighting its robustness.
Comprehensive evaluations confirmed a strong predictive reliability, with an average validation rate of 80% for case studies on common anticancer drugs.
The framework employs a dual-masked strategy combined with an adaptive contrastive loss, enhancing learning against disruptions and noise.
This innovative approach supports discoveries in circRNA-drug associations and may aid in precision therapy design for cancer treatment.

Wang et al. (2025) studied this question.

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November 30, 2025Frontiers in GeneticsOpen Access

View Full Paper

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Authors

Analysis shows improved drug response prediction in drug-sensitive models, indicating a novel framework for circRNA-drug interactions.

DMAGCL achieves state-of-the-art performance with an average AUC of 0.8940 across five-fold cross-validation, highlighting its robustness.
Comprehensive evaluations confirmed a strong predictive reliability, with an average validation rate of 80% for case studies on common anticancer drugs.
The framework employs a dual-masked strategy combined with an adaptive contrastive loss, enhancing learning against disruptions and noise.
This innovative approach supports discoveries in circRNA-drug associations and may aid in precision therapy design for cancer treatment.

Wang et al. (2025) studied this question.

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