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Causal discovery for the microbiome

Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various di...

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Autores principales: Corander, Jukka, Hanage, William P, Pensar, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680137/
https://www.ncbi.nlm.nih.gov/pubmed/36152674
http://dx.doi.org/10.1016/S2666-5247(22)00186-0
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author Corander, Jukka
Hanage, William P
Pensar, Johan
author_facet Corander, Jukka
Hanage, William P
Pensar, Johan
author_sort Corander, Jukka
collection PubMed
description Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch’s classical postulates remind us about the importance of causative reasoning when considering the relationship between microbes and a disease manifestation. Although causal discovery in observational microbiome data faces many challenges, methodological advances in causal structure learning have improved the potential of data-driven prediction of causal effects in large-scale biological systems. In this Personal View, we show the capability of existing methods for inferring causal effects from metagenomic data, and we highlight ways in which the introduction of causal structures that are more flexible than existing structures offers new opportunities for causal reasoning. Our observations suggest that microbiome research can further benefit from tools developed in the past 5 years in causal discovery and learn from their applications elsewhere.
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spelling pubmed-96801372022-11-22 Causal discovery for the microbiome Corander, Jukka Hanage, William P Pensar, Johan Lancet Microbe Article Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch’s classical postulates remind us about the importance of causative reasoning when considering the relationship between microbes and a disease manifestation. Although causal discovery in observational microbiome data faces many challenges, methodological advances in causal structure learning have improved the potential of data-driven prediction of causal effects in large-scale biological systems. In this Personal View, we show the capability of existing methods for inferring causal effects from metagenomic data, and we highlight ways in which the introduction of causal structures that are more flexible than existing structures offers new opportunities for causal reasoning. Our observations suggest that microbiome research can further benefit from tools developed in the past 5 years in causal discovery and learn from their applications elsewhere. 2022-11 2022-09-21 /pmc/articles/PMC9680137/ /pubmed/36152674 http://dx.doi.org/10.1016/S2666-5247(22)00186-0 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article under the CC BY 4.0 license.
spellingShingle Article
Corander, Jukka
Hanage, William P
Pensar, Johan
Causal discovery for the microbiome
title Causal discovery for the microbiome
title_full Causal discovery for the microbiome
title_fullStr Causal discovery for the microbiome
title_full_unstemmed Causal discovery for the microbiome
title_short Causal discovery for the microbiome
title_sort causal discovery for the microbiome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680137/
https://www.ncbi.nlm.nih.gov/pubmed/36152674
http://dx.doi.org/10.1016/S2666-5247(22)00186-0
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