Thesaurusterm: Klassieke fysica niet elders geclassificeerd
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| A1 publicaties (16) [show] |
 Rubbens, P.; Brodie, S.; Cordier, T.; Barcellos, D.D.; Devos, P.; Fernandes-Salvador, J.A.; Fincham, J.I.; Gomes, A.; Handegard, N.O.; Howell, K.; Jamet, C.; Kartveit, K.H.; Moustahfid, H.; Parcerisas, C.; Politikos, D.; Sauzède, R.; Sokolova, M.; Uusitalo, L.; Van den Bulcke, L.; van Helmond, A.T.M.; Watson, J.T.; Welch, H.; Beltran-Perez, O.; Chaffron, S.; Greenberg, D.S.; Kühn, B.; Kiko, R.; Lo, M.; Lopes, R.M.; Möller, K.O.; Michaels, W.; Pala, A.; Romagnan, J.-B.; Schuchert, P.; Seydi, V.; Villasante, S.; Malde, K.; Irisson, J.-O. (2023). Machine learning in marine ecology: An overview of techniques and applications. ICES J. Mar. Sci./J. Cons. int. Explor. Mer 80(7): 1829-1853. https://dx.doi.org/10.1093/icesjms/fsad100, meer- 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). 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
- 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. (2021). Computational analysis of microbial flow cytometry data. mSystems 6(1): e00895-20. https://dx.doi.org/10.1128/msystems.00895-20, 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
- Czechowska, Kamila; Lannigan, Joanne; Wang, Lili; Arcidiacono, Judith; Ashhurst, Thomas M.; Barnard, Ruth M.; Bauer, Steven; Bispo, Cláudia; Bonilla, Diana L.; Brinkman, Ryan R.; Cabanski, Maciej; Chang, Hyun‐Dong; Chakrabarti, Lina; Chojnowski, Grace; Cotleur, Bunny; Degheidy, Heba; Dela Cruz, Gelo V.; Eck, Steven; Elliott, John; Errington, Rachel; Filby, Andy; Gagnon, Dominic; Gardner, Rui; Green, Cherie; Gregory, Michael; Groves, Christopher J.; Hall, Christopher; Hammes, Frederik; Hedrick, Michael; Hoffman, Robert; Icha, Jaroslav; Ivaska, Johanna; Jenner, Dominic C.; Jones, Derek; Kerckhof, Frederiek‐Maarten; Kukat, Christian; Lanham, David; Leavesley, Silas; Lee, Michael; Lin‐Gibson, Sheng; Litwin, Virginia; Liu, Yanli; Molloy, Jenny; Moore, Jonni S.; Müller, Susann; Nedbal, Jakub; Niesner, Raluca; Nitta, Nao; Ohlsson‐Wilhelm, Betsy; Paul, Nicole E.; Perfetto, Stephen; Portat, Ziv; Props, Ruben; Radtke, Stefan; Rayanki, Radhika; Rieger, Aja; Rogers, Samson; Rubbens, Peter; Salomon, Robert; Schiemann, Matthias; Sharpe, John; Sonder, Soren Ulrik; Stewart, Jennifer J.; Sun, Yongliang; Ulrich, Henning; Van Isterdael, Gert; Vitaliti, Alessandra; Vreden, Caryn; Weber, Michael; Zimmermann, Jakob; Vacca, Giacomo; Wallace, Paul; Tárnok, Attila (2019). Cyt‐Geist: current and future challenges in cytometry: reports of the CYTO 2018 conference workshops. Cytometry A 95(6): 598-644. https://dx.doi.org/10.1002/cyto.a.23777, 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
- Props, R.; Kerckhof, F.-M.; Rubbens, P.; De Vrieze, J.; Hernandez Sanabria, E.; Waegeman, W.; Monsieurs, P.; Hammes, F.; Boon, N. (2017). Absolute quantification of microbial taxon abundances. ISME J. 11(2): 584-587. https://dx.doi.org/10.1038/ismej.2016.117, meer
- Rubbens, P.; Props, R.; Boon, N.; Waegeman, W. (2017). Flow cytometric single-cell identification of populations in synthetic bacterial communities. PLoS One 12(1): e0169754. https://dx.doi.org/10.1371/journal.pone.0169754, meer
- Rubbens, P.; Props, R.; Garcia-Timermans, C.; Boon, N.; Waegeman, W. (2017). Stripping flow cytometry: how many detectors do we need for bacterial identification? Cytometry A 91(12): 1184-1191. https://dx.doi.org/10.1002/cyto.a.23284, meer
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| Boek [show] |
- Devriese, L.I.; Pirlet, H.; Nauwynck, H.; Janssen, C.; Boon, N.; Arends, J.B.A.; Maelfait, H.; Rubbens, P.; Vandegehuchte, M.; Verleye, T.; Lescrauwaet, A.-K.; Mees, J. (2020). Fact Check. Wetenschappelijke kennis over het coronavirus SARS-CoV-2 in de context van de Vlaamse stranden. VLIZ Beleidsinformerende Nota's, 2020_003. Vlaams Instituut voor de Zee (VLIZ): Oostende. ISBN 9789492043948. 25 pp., meer
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| Thesissen (3) [show] |
- Rubbens, P. (2019). Machine learning approaches for microbial flow cytometry at the single-cell and community level. PhD Thesis. Ghent University. Faculty of Bioscience Engineering: Ghent. ISBN 9789463572408. xxiv, 240 pp., meer
- Rubbens, P. (2015). Theory choice in physics. Postgraduate Thesis. University of Ghent, Faculty of Arts and Philosophy: Ghent. ii, 38 pp., meer
- Rubbens, P. (2013). Competitie - Coördinatie en de dynamiek van wetenschappelijke revoluties. MA Thesis. Universiteit Gent. Faculteit Wetenschappen: Gent. xii, 149 pp., meer
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| Abstract [show] |
- 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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