Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.

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Autores principales:	Arman Eshaghi, Alexandra L. Young, Peter A. Wijeratne, Ferran Prados, Douglas L. Arnold, Sridar Narayanan, Charles R. G. Guttmann, Frederik Barkhof, Daniel C. Alexander, Alan J. Thompson, Declan Chard, Olga Ciccarelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b925ea625e9146fc8c9474dea8ca687b
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spelling	oai:doaj.org-article:b925ea625e9146fc8c9474dea8ca687b2021-12-02T18:15:35ZIdentifying multiple sclerosis subtypes using unsupervised machine learning and MRI data10.1038/s41467-021-22265-22041-1723https://doaj.org/article/b925ea625e9146fc8c9474dea8ca687b2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22265-2https://doaj.org/toc/2041-1723Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.Arman EshaghiAlexandra L. YoungPeter A. WijeratneFerran PradosDouglas L. ArnoldSridar NarayananCharles R. G. GuttmannFrederik BarkhofDaniel C. AlexanderAlan J. ThompsonDeclan ChardOlga CiccarelliNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Arman Eshaghi Alexandra L. Young Peter A. Wijeratne Ferran Prados Douglas L. Arnold Sridar Narayanan Charles R. G. Guttmann Frederik Barkhof Daniel C. Alexander Alan J. Thompson Declan Chard Olga Ciccarelli Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
description	Multiple sclerosis is a heterogeneous progressive disease. Here, the authors use an unsupervised machine learning algorithm to determine multiple sclerosis subtypes, progression, and response to potential therapeutic treatments based on neuroimaging data.
format	article
author	Arman Eshaghi Alexandra L. Young Peter A. Wijeratne Ferran Prados Douglas L. Arnold Sridar Narayanan Charles R. G. Guttmann Frederik Barkhof Daniel C. Alexander Alan J. Thompson Declan Chard Olga Ciccarelli
author_facet	Arman Eshaghi Alexandra L. Young Peter A. Wijeratne Ferran Prados Douglas L. Arnold Sridar Narayanan Charles R. G. Guttmann Frederik Barkhof Daniel C. Alexander Alan J. Thompson Declan Chard Olga Ciccarelli
author_sort	Arman Eshaghi
title	Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
title_short	Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
title_full	Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
title_fullStr	Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
title_full_unstemmed	Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data
title_sort	identifying multiple sclerosis subtypes using unsupervised machine learning and mri data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b925ea625e9146fc8c9474dea8ca687b
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Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

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