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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning
- Source :
- Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
- Publication Year :
- 2020
- Publisher :
- Nature Portfolio, 2020.
-
Abstract
- 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.
- Subjects :
- Science
Subjects
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 11
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Nature Communications
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.41c9c9671084acbbaf16ef58f178d60
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41467-020-19673-1