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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-19673-1 |
| _version_ | 1818851532182913024 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T07:06:31Z |
| format | Article |
| id | doaj.art-e41c9c9671084acbbaf16ef58f178d60 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-19T07:06:31Z |
| publishDate | 2020-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-e41c9c9671084acbbaf16ef58f178d602022-12-21T20:31:16ZengNature PortfolioNature Communications2041-17232020-11-0111111110.1038/s41467-020-19673-1Automatic classification and segmentation of single-molecule fluorescence time traces with deep learningJieming Li0Leyou Zhang1Alexander Johnson-Buck2Nils G. Walter3Single Molecule Analysis Group, Department of Chemistry and Center for RNA Biomedicine, The University of MichiganDepartment of Physics, The University of MichiganSingle Molecule Analysis Group, Department of Chemistry and Center for RNA Biomedicine, The University of MichiganSingle Molecule Analysis Group, Department of Chemistry and Center for RNA Biomedicine, The University of MichiganTraces 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.https://doi.org/10.1038/s41467-020-19673-1 |
| spellingShingle | Jieming Li Leyou Zhang Alexander Johnson-Buck Nils G. Walter Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning Nature Communications |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41467-020-19673-1 |
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