Universiteit Gent; Faculteit Bio-ingenieurswetenschappen; Vakgroep Data-analyse en Wiskundige Modellering, meer
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| A1 publicaties (10) [show] |
 Heyse, J.; Schattenberg, F.; Rubbens, P.; Müller, S.; Waegeman, W.; Boon, N.; Props, R. (2021). Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting. mSystems 6(5): e00551-21. https://dx.doi.org/10.1128/msystems.00551-21, meer- Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W. (2021). PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes inmicrobial community structure. mSphere 6(1): e00530-20. https://dx.doi.org/10.1128/msphere.00530-20, meer
- Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W. (2021). Cytometric fingerprints of gut microbiota predict Crohn’s disease state. ISME J. 15(1): 354-358. https://dx.doi.org/10.1038/s41396-020-00762-4, meer
- García-Timermans, C.; Rubbens, P.; Heyse, J.; Kerckhof, F.-M.; Props, R.; Skirtach, A.G.; Waegeman, W.; Boon, N. (2020). Discriminating bacterial phenotypes at the population and single‐cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting. Cytometry A 97(7): 713-726. https://dx.doi.org/10.1002/cyto.a.23952, meer
- Papagiannopoulou, C.; Parchen, R.; Rubbens, P.; Waegeman, W. (2020). Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. Anal. Chem. 92(11): 7523-7531. https://dx.doi.org/10.1021/acs.analchem.9b05806, meer
- Heyse, J.; Buysschaert, B.; Props, R.; Rubbens, P.; Skirtach, A.G.; Waegeman, W.; Boon, N. (2019). Coculturing bacteria leads to reduced phenotypic heterogeneities. Appl. Environ. Microbiol. 85(8): e02814-18. https://dx.doi.org/10.1128/aem.02814-18, meer
- Nguyen, B.; Rubbens, P.; Kerckhof, F.-M.; Boon, N.; De Baets, B.; Waegeman, W. (2019). Learning single‐cell distances from cytometry data. Cytometry A 95(7): 782-791. https://dx.doi.org/10.1002/cyto.a.23792, meer
- Rubbens, P.; Schmidt, M.L.; Props, R.; Biddanda, B.A.; Boon, N.; Waegeman, W.; Denef, V.J. (2019). Randomized Lasso links microbial taxa with aquatic functional groups inferred from flow cytometry. mSystems 4(5): e00093-19. https://dx.doi.org/10.1128/msystems.00093-19, meer
- García-Timermans, C.; Rubbens, P.; Kerckhof, F.-M.; Buysschaert, B.; Khalenkow, D.; Waegeman, W.; Skirtach, A.G.; Boon, N. (2018). Label-free Raman characterization of bacteria calls for standardized procedures. J. microbiol. methods 151: 69-75. https://dx.doi.org/10.1016/j.mimet.2018.05.027, meer
- Props, R.; Rubbens, P.; Besmer, M.; Buysschaert, B.; Sigrist, J.; Weilenmann, H.; Waegeman, W.; Boon, N.; Hammes, F. (2018). Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data. Wat. Res. 145: 73-82. https://dx.doi.org/10.1016/j.watres.2018.08.013, meer
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| Abstracts (2) [show] |
- Van den Bulcke, L.; De Backer, A.; Hostens, K.; Maes, S.; Waegeman, W.; Derycke, S. (2020). Implementation of DNA metabarcoding in environmental impact assessments, in: Mees, J. et al. Book of abstracts – VLIZ Marine Science Day. Oostende, Belgium, 18 March 2020. VLIZ Special Publication, 84: pp. 82, meer
- García-Timermans, C.; Rubbens, P.; Kerckhof, F.M.; Waegeman, W.; Boon, N. (2019). Fingerprinting microbial communities through flow cytometry and Raman spectroscopy, in: BAGECO 15. 15th Symposium on Bacterial Genetics and Ecology: "Ecosystem drivers in a changing planet", 26–30 May 2019, Lisbon/Portugal. pp. 155, meer
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