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Network Analysis in Systems Epidemiology

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect...

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Autores principales: Park, JooYong, Choi, Jaesung, Choi, Ji-Yeob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society for Preventive Medicine 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357545/
https://www.ncbi.nlm.nih.gov/pubmed/34370939
http://dx.doi.org/10.3961/jpmph.21.190
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author Park, JooYong
Choi, Jaesung
Choi, Ji-Yeob
author_facet Park, JooYong
Choi, Jaesung
Choi, Ji-Yeob
author_sort Park, JooYong
collection PubMed
description Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.
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spelling pubmed-83575452021-08-13 Network Analysis in Systems Epidemiology Park, JooYong Choi, Jaesung Choi, Ji-Yeob J Prev Med Public Health Special Article Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the “black-box” aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings. Korean Society for Preventive Medicine 2021-07 2021-07-07 /pmc/articles/PMC8357545/ /pubmed/34370939 http://dx.doi.org/10.3961/jpmph.21.190 Text en Copyright © 2021 The Korean Society for Preventive Medicine https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Special Article
Park, JooYong
Choi, Jaesung
Choi, Ji-Yeob
Network Analysis in Systems Epidemiology
title Network Analysis in Systems Epidemiology
title_full Network Analysis in Systems Epidemiology
title_fullStr Network Analysis in Systems Epidemiology
title_full_unstemmed Network Analysis in Systems Epidemiology
title_short Network Analysis in Systems Epidemiology
title_sort network analysis in systems epidemiology
topic Special Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357545/
https://www.ncbi.nlm.nih.gov/pubmed/34370939
http://dx.doi.org/10.3961/jpmph.21.190
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