A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network
Document Type
Article
Publication Date
7-1-2024
Abstract
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
Divisions
fac_eng
Funders
National Key Research & Development Program of China (2020YFC2006600),National Natural Science Foundation of China (NSFC) (62003291),National Science and Technology Foundation Project (2019FY100100),QingLan Project of Jiangsu Province of China
Publication Title
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume
21
Issue
4
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA