Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically make analysis time-consuming. Here, the authors have developed an easily accessible software, AutoSiM, for two distinct applications of deep learning to the efficient processing of S...

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Autores principales:	Jieming Li, Leyou Zhang, Alexander Johnson-Buck, Nils G. Walter
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e41c9c9671084acbbaf16ef58f178d60
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spelling	oai:doaj.org-article:e41c9c9671084acbbaf16ef58f178d602021-12-02T15:39:12ZAutomatic classification and segmentation of single-molecule fluorescence time traces with deep learning10.1038/s41467-020-19673-12041-1723https://doaj.org/article/e41c9c9671084acbbaf16ef58f178d602020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19673-1https://doaj.org/toc/2041-1723Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically make analysis time-consuming. Here, the authors have developed an easily accessible software, AutoSiM, for two distinct applications of deep learning to the efficient processing of SMFM time traces.Jieming LiLeyou ZhangAlexander Johnson-BuckNils G. WalterNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jieming Li Leyou Zhang Alexander Johnson-Buck Nils G. Walter Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
description	Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically make analysis time-consuming. Here, the authors have developed an easily accessible software, AutoSiM, for two distinct applications of deep learning to the efficient processing of SMFM time traces.
format	article
author	Jieming Li Leyou Zhang Alexander Johnson-Buck Nils G. Walter
author_facet	Jieming Li Leyou Zhang Alexander Johnson-Buck Nils G. Walter
author_sort	Jieming Li
title	Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
title_short	Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
title_full	Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
title_fullStr	Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
title_full_unstemmed	Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
title_sort	automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e41c9c9671084acbbaf16ef58f178d60
work_keys_str_mv	AT jiemingli automaticclassificationandsegmentationofsinglemoleculefluorescencetimetraceswithdeeplearning AT leyouzhang automaticclassificationandsegmentationofsinglemoleculefluorescencetimetraceswithdeeplearning AT alexanderjohnsonbuck automaticclassificationandsegmentationofsinglemoleculefluorescencetimetraceswithdeeplearning AT nilsgwalter automaticclassificationandsegmentationofsinglemoleculefluorescencetimetraceswithdeeplearning
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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning

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