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A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases
Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combinatio...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050263/ https://www.ncbi.nlm.nih.gov/pubmed/35495886 http://dx.doi.org/10.1155/2022/4471736 |
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author | Wang, Huanhuan Yin, Hongsheng Wu, Xiang |
author_facet | Wang, Huanhuan Yin, Hongsheng Wu, Xiang |
author_sort | Wang, Huanhuan |
collection | PubMed |
description | Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy. |
format | Online Article Text |
id | pubmed-9050263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90502632022-04-29 A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases Wang, Huanhuan Yin, Hongsheng Wu, Xiang Comput Math Methods Med Research Article Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy. Hindawi 2022-04-21 /pmc/articles/PMC9050263/ /pubmed/35495886 http://dx.doi.org/10.1155/2022/4471736 Text en Copyright © 2022 Huanhuan Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Huanhuan Yin, Hongsheng Wu, Xiang A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title | A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title_full | A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title_fullStr | A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title_full_unstemmed | A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title_short | A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases |
title_sort | secure high-order gene interaction detecting method for infectious diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050263/ https://www.ncbi.nlm.nih.gov/pubmed/35495886 http://dx.doi.org/10.1155/2022/4471736 |
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