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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2020
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| Materias: | |
| 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 |