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    Area of Science:

    • Biochemistry
    • Genomics
    • Computational Biology

    Background:

    • Circular RNAs (circRNAs) are stable noncoding RNAs with roles in cancer pathogenesis, acting as microRNA sponges.
    • circRNAs are recognized as potential biomarkers and drug targets for cancer resistance.
    • Current methods for identifying circRNA-drug resistance associations are slow and costly.

    Purpose of the Study:

    • To develop an efficient computational framework for predicting circRNA-drug resistance associations.
    • To incorporate disease-specific information to enhance prediction accuracy.
    • To accelerate the discovery and development of circRNA-targeting cancer drugs.

    Main Methods:

    • Proposed GraphCDD, a computational framework integrating circRNA, disease, and drug data.
    • Constructed three similarity networks representing circRNA, disease, and drug features.
    • Employed a multimodal graph neural network for integrated data representation and prediction.

    Main Results:

    • The GraphCDD framework effectively predicted associations between circRNAs and drug resistance.
    • Experimental results validated the model's performance.
    • The study demonstrated the utility of integrating disease information for improved predictions.

    Conclusions:

    • GraphCDD offers a promising computational approach for identifying circRNA-drug resistance links.
    • Integrating disease-related information enhances the prediction of circRNA-drug resistance associations.
    • This framework can expedite the development of targeted cancer therapies.