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Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes–microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD di...

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Autores principales: Jiang, Ziyuan, Li, Jiajin, Kong, Nahyun, Kim, Jeong-Hyun, Kim, Bong-Soo, Lee, Min-Jung, Park, Yoon Mee, Lee, So-Yeon, Hong, Soo-Jong, Sul, Jae Hoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741793/
https://www.ncbi.nlm.nih.gov/pubmed/34997172
http://dx.doi.org/10.1038/s41598-021-04373-7
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author Jiang, Ziyuan
Li, Jiajin
Kong, Nahyun
Kim, Jeong-Hyun
Kim, Bong-Soo
Lee, Min-Jung
Park, Yoon Mee
Lee, So-Yeon
Hong, Soo-Jong
Sul, Jae Hoon
author_facet Jiang, Ziyuan
Li, Jiajin
Kong, Nahyun
Kim, Jeong-Hyun
Kim, Bong-Soo
Lee, Min-Jung
Park, Yoon Mee
Lee, So-Yeon
Hong, Soo-Jong
Sul, Jae Hoon
author_sort Jiang, Ziyuan
collection PubMed
description Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes–microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction.
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spelling pubmed-87417932022-01-10 Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning Jiang, Ziyuan Li, Jiajin Kong, Nahyun Kim, Jeong-Hyun Kim, Bong-Soo Lee, Min-Jung Park, Yoon Mee Lee, So-Yeon Hong, Soo-Jong Sul, Jae Hoon Sci Rep Article Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes–microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction. Nature Publishing Group UK 2022-01-07 /pmc/articles/PMC8741793/ /pubmed/34997172 http://dx.doi.org/10.1038/s41598-021-04373-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jiang, Ziyuan
Li, Jiajin
Kong, Nahyun
Kim, Jeong-Hyun
Kim, Bong-Soo
Lee, Min-Jung
Park, Yoon Mee
Lee, So-Yeon
Hong, Soo-Jong
Sul, Jae Hoon
Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title_full Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title_fullStr Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title_full_unstemmed Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title_short Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
title_sort accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741793/
https://www.ncbi.nlm.nih.gov/pubmed/34997172
http://dx.doi.org/10.1038/s41598-021-04373-7
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