Machine learning for cluster analysis of localization microscopy data

The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.

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Autores principales:	David J. Williamson, Garth L. Burn, Sabrina Simoncelli, Juliette Griffié, Ruby Peters, Daniel M. Davis, Dylan M. Owen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5bc574d8c2a043d098ecae44cf7959d5
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spelling	oai:doaj.org-article:5bc574d8c2a043d098ecae44cf7959d52021-12-02T15:39:17ZMachine learning for cluster analysis of localization microscopy data10.1038/s41467-020-15293-x2041-1723https://doaj.org/article/5bc574d8c2a043d098ecae44cf7959d52020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15293-xhttps://doaj.org/toc/2041-1723The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.David J. WilliamsonGarth L. BurnSabrina SimoncelliJuliette GriffiéRuby PetersDaniel M. DavisDylan M. OwenNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q David J. Williamson Garth L. Burn Sabrina Simoncelli Juliette Griffié Ruby Peters Daniel M. Davis Dylan M. Owen Machine learning for cluster analysis of localization microscopy data
description	The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.
format	article
author	David J. Williamson Garth L. Burn Sabrina Simoncelli Juliette Griffié Ruby Peters Daniel M. Davis Dylan M. Owen
author_facet	David J. Williamson Garth L. Burn Sabrina Simoncelli Juliette Griffié Ruby Peters Daniel M. Davis Dylan M. Owen
author_sort	David J. Williamson
title	Machine learning for cluster analysis of localization microscopy data
title_short	Machine learning for cluster analysis of localization microscopy data
title_full	Machine learning for cluster analysis of localization microscopy data
title_fullStr	Machine learning for cluster analysis of localization microscopy data
title_full_unstemmed	Machine learning for cluster analysis of localization microscopy data
title_sort	machine learning for cluster analysis of localization microscopy data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/5bc574d8c2a043d098ecae44cf7959d5
work_keys_str_mv	AT davidjwilliamson machinelearningforclusteranalysisoflocalizationmicroscopydata AT garthlburn machinelearningforclusteranalysisoflocalizationmicroscopydata AT sabrinasimoncelli machinelearningforclusteranalysisoflocalizationmicroscopydata AT juliettegriffie machinelearningforclusteranalysisoflocalizationmicroscopydata AT rubypeters machinelearningforclusteranalysisoflocalizationmicroscopydata AT danielmdavis machinelearningforclusteranalysisoflocalizationmicroscopydata AT dylanmowen machinelearningforclusteranalysisoflocalizationmicroscopydata
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Machine learning for cluster analysis of localization microscopy data

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