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    This study introduces a novel graph matching model to uncover complex gene-drug interactions, advancing precise drug repurposing and cancer pathway discovery. The method effectively identifies multivariate gene-drug regulatory modules from large datasets.

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    Area of Science:

    • Genomics
    • Pharmacology
    • Bioinformatics

    Background:

    • Identifying gene-drug interactions is crucial for understanding biological mechanisms and enabling precise drug repurposing.
    • High-throughput technologies generate vast genomic and pharmacological data, offering opportunities to explore oncogenic gene-therapeutic drug associations.
    • Existing methods often overlook complex, multivariate gene-drug patterns, focusing primarily on one-to-one or one-to-many interactions.

    Purpose of the Study:

    • To develop a novel computational model for discovering gene-drug common regulatory modules that captures multivariate relationships.
    • To incorporate prior biological knowledge into a hypergraph framework to enhance the identification of complex gene-drug correspondences.
    • To evaluate the model's performance and robustness on both synthetic and real-world pharmacogenomics data.

    Main Methods:

    • A high-order graph matching model incorporating hypergraph constraints was developed.
    • Prior biological knowledge was formulated into hypergraph constraints to guide the tensor matching process.
    • The model was tested on synthetic data for robustness against noise and outliers, and on pharmacogenomics data for biological relevance.

    Main Results:

    • The proposed model demonstrated superior performance compared to four state-of-the-art methods on synthetic data, showing robustness to noise and outliers.
    • Analysis of pharmacogenomics data identified significant gene-drug common modules.
    • These modules were associated with enriched cancer-related pathways and revealed close gene-drug interactions.

    Conclusions:

    • The developed high-order graph matching model with hypergraph constraints effectively identifies complex, multivariate gene-drug regulatory modules.
    • This approach advances the field of precise drug repurposing and enhances our understanding of gene-drug relationships in cancer.
    • The method shows strong statistical power and applicability to real-world pharmacogenomics data for discovering biologically relevant modules.