Machine learning approaches for microbial flow cytometry at the single-cell and community level
(2019)
- Author
- Peter Rubbens
- Promoter
- Willem Waegeman (UGent) and Nico Boon (UGent)
- Organization
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8628541
- MLA
- Rubbens, Peter. Machine Learning Approaches for Microbial Flow Cytometry at the Single-Cell and Community Level. Ghent University. Faculty of Bioscience Engineering, 2019.
- APA
- Rubbens, P. (2019). Machine learning approaches for microbial flow cytometry at the single-cell and community level. Ghent University. Faculty of Bioscience Engineering, Ghent, Belgium.
- Chicago author-date
- Rubbens, Peter. 2019. “Machine Learning Approaches for Microbial Flow Cytometry at the Single-Cell and Community Level.” Ghent, Belgium: Ghent University. Faculty of Bioscience Engineering.
- Chicago author-date (all authors)
- Rubbens, Peter. 2019. “Machine Learning Approaches for Microbial Flow Cytometry at the Single-Cell and Community Level.” Ghent, Belgium: Ghent University. Faculty of Bioscience Engineering.
- Vancouver
- 1.Rubbens P. Machine learning approaches for microbial flow cytometry at the single-cell and community level. [Ghent, Belgium]: Ghent University. Faculty of Bioscience Engineering; 2019.
- IEEE
- [1]P. Rubbens, “Machine learning approaches for microbial flow cytometry at the single-cell and community level,” Ghent University. Faculty of Bioscience Engineering, Ghent, Belgium, 2019.
@phdthesis{8628541,
author = {{Rubbens, Peter}},
isbn = {{9789463572408}},
language = {{eng}},
pages = {{XXIV, 240}},
publisher = {{Ghent University. Faculty of Bioscience Engineering}},
school = {{Ghent University}},
title = {{Machine learning approaches for microbial flow cytometry at the single-cell and community level}},
year = {{2019}},
}