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Identification of Asthma Subtypes Using Clustering Methodologies
Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Healthcare
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959136/ https://www.ncbi.nlm.nih.gov/pubmed/27512723 http://dx.doi.org/10.1007/s41030-016-0017-z |
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author | Deliu, Matea Sperrin, Matthew Belgrave, Danielle Custovic, Adnan |
author_facet | Deliu, Matea Sperrin, Matthew Belgrave, Danielle Custovic, Adnan |
author_sort | Deliu, Matea |
collection | PubMed |
description | Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches. |
format | Online Article Text |
id | pubmed-4959136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-49591362016-08-08 Identification of Asthma Subtypes Using Clustering Methodologies Deliu, Matea Sperrin, Matthew Belgrave, Danielle Custovic, Adnan Pulm Ther Review Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches. Springer Healthcare 2016-06-22 2016 /pmc/articles/PMC4959136/ /pubmed/27512723 http://dx.doi.org/10.1007/s41030-016-0017-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Review Deliu, Matea Sperrin, Matthew Belgrave, Danielle Custovic, Adnan Identification of Asthma Subtypes Using Clustering Methodologies |
title | Identification of Asthma Subtypes Using Clustering Methodologies |
title_full | Identification of Asthma Subtypes Using Clustering Methodologies |
title_fullStr | Identification of Asthma Subtypes Using Clustering Methodologies |
title_full_unstemmed | Identification of Asthma Subtypes Using Clustering Methodologies |
title_short | Identification of Asthma Subtypes Using Clustering Methodologies |
title_sort | identification of asthma subtypes using clustering methodologies |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959136/ https://www.ncbi.nlm.nih.gov/pubmed/27512723 http://dx.doi.org/10.1007/s41030-016-0017-z |
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