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Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network

The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis an...

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Autores principales: Shen, Fei, Cai, Wensong, Gan, Xiaoxiong, Feng, Jianhua, Chen, Zhen, Guo, Mengli, Wei, Fang, Cao, Jie, Xu, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365469/
https://www.ncbi.nlm.nih.gov/pubmed/34409035
http://dx.doi.org/10.3389/fcell.2021.700355
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author Shen, Fei
Cai, Wensong
Gan, Xiaoxiong
Feng, Jianhua
Chen, Zhen
Guo, Mengli
Wei, Fang
Cao, Jie
Xu, Bo
author_facet Shen, Fei
Cai, Wensong
Gan, Xiaoxiong
Feng, Jianhua
Chen, Zhen
Guo, Mengli
Wei, Fang
Cao, Jie
Xu, Bo
author_sort Shen, Fei
collection PubMed
description The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.
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spelling pubmed-83654692021-08-17 Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network Shen, Fei Cai, Wensong Gan, Xiaoxiong Feng, Jianhua Chen, Zhen Guo, Mengli Wei, Fang Cao, Jie Xu, Bo Front Cell Dev Biol Cell and Developmental Biology The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called “RW-RVM,” is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method. Frontiers Media S.A. 2021-08-02 /pmc/articles/PMC8365469/ /pubmed/34409035 http://dx.doi.org/10.3389/fcell.2021.700355 Text en Copyright © 2021 Shen, Cai, Gan, Feng, Chen, Guo, Wei, Cao and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Shen, Fei
Cai, Wensong
Gan, Xiaoxiong
Feng, Jianhua
Chen, Zhen
Guo, Mengli
Wei, Fang
Cao, Jie
Xu, Bo
Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title_full Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title_fullStr Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title_full_unstemmed Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title_short Prediction of Genetic Factors of Hyperthyroidism Based on Gene Interaction Network
title_sort prediction of genetic factors of hyperthyroidism based on gene interaction network
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365469/
https://www.ncbi.nlm.nih.gov/pubmed/34409035
http://dx.doi.org/10.3389/fcell.2021.700355
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