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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...

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Detalles Bibliográficos
Autores principales: Deliu, Matea, Sperrin, Matthew, Belgrave, Danielle, Custovic, Adnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2016
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.
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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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