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A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network.

Yongting Zhang, Yonggang Gao, Huanhuan Wang

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    This study introduces Deep-DPGI, a novel framework using Deep Learning and Differential Privacy to detect complex high-order gene interactions. It enhances disease gene discovery while effectively protecting sensitive genetic information.

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    Area of Science:

    • Genetics
    • Bioinformatics
    • Computational Biology

    Background:

    • Identifying complex genetic interactions is vital for understanding disease mechanisms.
    • Current methods struggle with high-order Single Nucleotide Polymorphism (SNP) interactions and data privacy.

    Purpose of the Study:

    • To develop a novel framework, Deep-DPGI, for detecting high-order gene interactions using Deep Learning (DL) and Differential Privacy (DP).
    • To enhance the accuracy of complex disease gene detection and prediction while ensuring data security.

    Main Methods:

    • Integration of cross-entropy and focal loss functions for model training.
    • Layer-wise relevance analysis to guide adaptive noising for neuron weights.
    • Differential Privacy (DP) implementation to protect high-order gene interaction data.

    Main Results:

    • Deep-DPGI effectively detects high-order gene interactions in complex disease models, including those with marginal and non-marginal effects.
    • The framework successfully balances privacy protection with the utility of genetic data.
    • Experiments on simulated and real datasets validate the model's performance.

    Conclusions:

    • Deep-DPGI offers a powerful and privacy-preserving approach for high-order gene interaction detection.
    • This method advances the identification of pathogenic genes and prediction of complex diseases.
    • The adaptive noising mechanism ensures sensitive information is not disclosed.