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Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
View ORCID ProfileRomain F. Laine, Gemma Goodfellow, Laurence J. Young, Jon Travers, Danielle Carroll, Oliver Dibben, Helen Bright, Clemens F. Kaminski
doi: https://doi.org/10.1101/266551
Romain F. Laine
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambrige, CB2 3AS, UK;
Gemma Goodfellow
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambrige, CB2 3AS, UK;
Laurence J. Young
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambrige, CB2 3AS, UK;
Jon Travers
2MedImmune Ltd., Sir Aaron Klug Building, Granta Park, Cambridge, UK;
Danielle Carroll
3AstraZeneca, Liverpool, UK
Oliver Dibben
3AstraZeneca, Liverpool, UK
Helen Bright
3AstraZeneca, Liverpool, UK
Clemens F. Kaminski
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambrige, CB2 3AS, UK;
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Posted July 11, 2018.
Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
Romain F. Laine, Gemma Goodfellow, Laurence J. Young, Jon Travers, Danielle Carroll, Oliver Dibben, Helen Bright, Clemens F. Kaminski
bioRxiv 266551; doi: https://doi.org/10.1101/266551
Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure
Romain F. Laine, Gemma Goodfellow, Laurence J. Young, Jon Travers, Danielle Carroll, Oliver Dibben, Helen Bright, Clemens F. Kaminski
bioRxiv 266551; doi: https://doi.org/10.1101/266551
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