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 necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMF...

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Detalles Bibliográficos
Autores principales: Li, Jieming, Zhang, Leyou, Johnson-Buck, Alexander, Walter, Nils G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673028/
https://www.ncbi.nlm.nih.gov/pubmed/33203879
http://dx.doi.org/10.1038/s41467-020-19673-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673028/
https://www.ncbi.nlm.nih.gov/pubmed/33203879
http://dx.doi.org/10.1038/s41467-020-19673-1

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