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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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| Pubblicato in: | Nat Commun |
|---|---|
| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Nature Publishing Group UK
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7673028/ https://ncbi.nlm.nih.gov/pubmed/33203879 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-19673-1 |
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