Peter Rubbens
Peter Rubbens
Unknown affiliation
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Cited by
Cited by
Absolute quantification of microbial taxon abundances
R Props, FM Kerckhof, P Rubbens, J De Vrieze, E Hernandez Sanabria, ...
The ISME journal 11 (2), 584-587, 2017
Flow cytometric single-cell identification of populations in synthetic bacterial communities
P Rubbens, R Props, N Boon, W Waegeman
PloS one 12 (1), e0169754, 2017
Label-free Raman characterization of bacteria calls for standardized procedures
C García-Timermans, P Rubbens, FM Kerckhof, B Buysschaert, ...
Journal of microbiological methods 151, 69-75, 2018
Coculturing bacteria leads to reduced phenotypic heterogeneities
J Heyse, B Buysschaert, R Props, P Rubbens, AG Skirtach, W Waegeman, ...
Applied and environmental microbiology 85 (8), e02814-18, 2019
Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data
R Props, P Rubbens, M Besmer, B Buysschaert, J Sigrist, H Weilenmann, ...
Water research 145, 73-82, 2018
Cyt‐Geist: Current and future challenges in cytometry: Reports of the CYTO 2018 Conference Workshops
K Czechowska, J Lannigan, L Wang, J Arcidiacono, TM Ashhurst, ...
Cytometry Part A 95 (6), 598-644, 2019
Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods
C Papagiannopoulou, R Parchen, P Rubbens, W Waegeman
Analytical chemistry 92 (11), 7523-7531, 2020
Stripping flow cytometry: How many detectors do we need for bacterial identification?
P Rubbens, R Props, C Garcia‐Timermans, N Boon, W Waegeman
Cytometry Part A 91 (12), 1184-1191, 2017
Discriminating bacterial phenotypes at the population and single‐cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting
C García‐Timermans, P Rubbens, J Heyse, FM Kerckhof, R Props, ...
Cytometry Part A 97 (7), 713-726, 2020
Cytometric fingerprints of gut microbiota predict Crohn’s disease state
P Rubbens, R Props, FM Kerckhof, N Boon, W Waegeman
The ISME Journal 15 (1), 354-358, 2021
Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry
P Rubbens, ML Schmidt, R Props, BA Biddanda, N Boon, W Waegeman, ...
MSystems 4 (5), e00093-19, 2019
PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure
P Rubbens, R Props, FM Kerckhof, N Boon, W Waegeman
Msphere 6 (1), e00530-20, 2021
Computational Analysis of Microbial Flow Cytometry Data
P Rubbens, R Props
mSystems 6 (1), 2021
From theory choice to theory search: the essential tension between exploration and exploitation in science
R De Langhe, P Rubbens
Kuhn’s Structure of Scientific Revolutions-50 Years On, 105-114, 2015
Learning single‐cell distances from cytometry data
B Nguyen, P Rubbens, FM Kerckhof, N Boon, B De Baets, W Waegeman
Cytometry Part A 95 (7), 782-791, 2019
Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting
J Heyse, F Schattenberg, P Rubbens, S Müller, W Waegeman, N Boon, ...
Msystems 6 (5), e00551-21, 2021
PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity
P Rubbens, R Props, FM Kerckhof, N Boon, W Waegeman
bioRxiv, 641464, 2020
Machine learning approaches for microbial flow cytometry at the single-cell and community level
P Rubbens
Ghent University, 2019
Real-Time Flow Cytometry to assess qualitative and quantitative responses of oral pathobionts during exposure to antiseptics
I Chatzigiannidou, J Heyse, R Props, P Rubbens, W Teughels, ...
bioRxiv, 2022
Fingerprinting microbial communities through flow cytometry and Raman spectroscopy
C García Timermans, P Rubbens, FM Kerckhof, R Props, W Waegeman, ...
15th Symposium on Bacterial Genetics and Ecology (BAGECO 15): Ecosystem …, 2019
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