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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...

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Detalles Bibliográficos
Autores principales: Wang, Huanhuan, Yin, Hongsheng, Wu, Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
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.
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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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