Zhang, Yongting and Gao, Yonggang and Wang, Huanhuan and Wu, Huaming and Xia, Youbing and Wu, Xiang (2024) A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21 (4). pp. 619-630. ISSN 1545-5963, DOI https://doi.org/10.1109/TCBB.2022.3214863.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| 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 |
| Uncontrolled Keywords: | Privacy; Training; Differential privacy; Computational modeling; Neurons; Data models; Genomics; Deep learning; differential privacy; gene interaction detection; Genome-Wide Association Studies |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Jan 2025 08:36 |
| Last Modified: | 20 Jan 2025 08:36 |
| URI: | http://eprints.um.edu.my/id/eprint/47575 |
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