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

Yongting Zhang

doi:10.1109/TCBB.2022.3214863

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

2024-07-01

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DOI：10.1109/TCBB.2022.3214863

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Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss. Secondly, use the layer-wise relevance analysis method to measure relevance difference between neurons weight and outputting results. Deep-DPGI disturbs neuron weight by adaptive noising mechanism, protecting the safety of high-order gene interactions and balancing the privacy and utility. Specifically, more noise is added to gradients of neurons that is less relevance with the outputs, less noise to gradients that more relevance. Finally, Experiments on simulated and real datasets demonstrate that Deep-DPGI not only improve the power of high-order gene interactions detection in with marginal and without marginal effect of complex disease models, but also prevent the disclosure of sensitive information effectively.

Keywords:

Privacy

Training

Differential privacy

Computational modeling

Neurons

Data models

Genomics

Deep learning

differential privacy

gene interaction detection

Genome-Wide Association Studies

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

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