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    Area of Science:

    • Genomics
    • Computational Biology
    • Biochemistry

    Background:

    • Disease etiology is complex, often involving gene mutations.
    • Wet lab experiments for mutation analysis are costly and limited in scale.
    • Predicting mutation-disease associations requires efficient computational methods.

    Purpose of the Study:

    • To develop a novel computational approach for predicting mutation-disease associations.
    • To elucidate the role of protein structural alterations in mutation-induced pathogenicity.
    • To create a real-world dataset for mutation-disease association studies.

    Main Methods:

    • Construction of a real-world mutation-induced disease dataset.
    • Development and application of Capsule and Graph topology networks with Multi-head attention (CGM).
    • Validation of CGM on benchmark and imbalanced datasets.

    Main Results:

    • CGM accurately predicts protein mutation-disease associations.
    • The model demonstrates that protein mutations can lead to structural alterations, a potential pathogenic factor.
    • CGM identified 22 unknown protein interaction pairs from a benchmark dataset.

    Conclusions:

    • CGM provides a novel and accurate tool for predicting mutation-disease associations.
    • Mutation-induced conformational changes are suggested as a key pathogenic mechanism.
    • The developed dataset and model advance the understanding of biomolecular pathways and disease mechanisms.