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A deep learning approach to capture the essence of Candida albicans morphologies
V Bettauer, ACBP Costa, RP Omran, S Massahi, E Kirbizakis, S Simpson, V Dumeaux, C Law, M Whiteway, MT Hallett
doi: https://doi.org/10.1101/2021.06.10.445299
V Bettauer
1Centre for Applied Synthetic Biology
3Department of Computer Science and Software Engineering
ACBP Costa
1Centre for Applied Synthetic Biology
2Department of Biology
RP Omran
1Centre for Applied Synthetic Biology
2Department of Biology
S Massahi
1Centre for Applied Synthetic Biology
2Department of Biology
E Kirbizakis
1Centre for Applied Synthetic Biology
2Department of Biology
S Simpson
1Centre for Applied Synthetic Biology
3Department of Computer Science and Software Engineering
V Dumeaux
2Department of Biology
4PERFORM Centre
C Law
5Centre for Microscopy and Cellular Imaging
M Whiteway
1Centre for Applied Synthetic Biology
2Department of Biology
6Centre for Structural and Functional Genomics Concordia University, Montreal, Quebec, Canada
MT Hallett
1Centre for Applied Synthetic Biology
2Department of Biology
6Centre for Structural and Functional Genomics Concordia University, Montreal, Quebec, Canada
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Posted June 10, 2021.
A deep learning approach to capture the essence of Candida albicans morphologies
V Bettauer, ACBP Costa, RP Omran, S Massahi, E Kirbizakis, S Simpson, V Dumeaux, C Law, M Whiteway, MT Hallett
bioRxiv 2021.06.10.445299; doi: https://doi.org/10.1101/2021.06.10.445299
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