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Comparative study of traditional and DNA-based methods for environmental impact assessment: A case study of marine aggregate extraction in the North Sea. <i>Sci. Total Environ. 946</i>: 174106. <a href=\"https://dx.doi.org/10.1016/j.scitotenv.2024.174106\" target=\"_blank\">https://dx.doi.org/10.1016/j.scitotenv.2024.174106</a>","AutID":571086,"MonDate":null,"AnaDate":2024,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":348868,"RR":"<b>Heyse, J.; Schattenberg, F.; Rubbens, P.; Müller, S.; Waegeman, W.; Boon, N.; Props, R.</b> (2021). Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting. <i>mSystems 6(5)</i>: e00551-21. <a href=\"https://dx.doi.org/10.1128/msystems.00551-21\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00551-21</a>","AutID":499296,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":348873,"RR":"<b>Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W.</b> (2021). PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes inmicrobial community structure. <i>mSphere 6(1)</i>: e00530-20. <a href=\"https://dx.doi.org/10.1128/msphere.00530-20\" target=\"_blank\">https://dx.doi.org/10.1128/msphere.00530-20</a>","AutID":499296,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":332287,"RR":"<b>Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W.</b> (2021). Cytometric fingerprints of gut microbiota predict Crohn’s disease state. <i>ISME J. 15(1)</i>: 354-358. <a href=\"https://dx.doi.org/10.1038/s41396-020-00762-4\" target=\"_blank\">https://dx.doi.org/10.1038/s41396-020-00762-4</a>","AutID":499296,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":321078,"RR":"<b>García-Timermans, C.; Rubbens, P.; Heyse, J.; Kerckhof, F.-M.; Props, R.; Skirtach, A.G.; Waegeman, W.; Boon, N.</b> (2020). Discriminating bacterial phenotypes at the population and single‐cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting. <i>Cytometry A 97(7)</i>: 713-726. <a href=\"https://dx.doi.org/10.1002/cyto.a.23952\" target=\"_blank\">https://dx.doi.org/10.1002/cyto.a.23952</a>","AutID":499296,"MonDate":null,"AnaDate":2020,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":324685,"RR":"<b>Papagiannopoulou, C.; Parchen, R.; Rubbens, P.; Waegeman, W.</b> (2020). Fast pathogen identification using single-cell matrix-assisted laser desorption/ionization-aerosol time-of-flight mass spectrometry data and deep learning methods. <i>Anal. Chem. 92(11)</i>: 7523-7531. <a href=\"https://dx.doi.org/10.1021/acs.analchem.9b05806\" target=\"_blank\">https://dx.doi.org/10.1021/acs.analchem.9b05806</a>","AutID":499296,"MonDate":null,"AnaDate":2020,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321900,"RR":"<b>Heyse, J.; Buysschaert, B.; Props, R.; Rubbens, P.; Skirtach, A.G.; Waegeman, W.; Boon, N.</b> (2019). Coculturing bacteria leads to reduced phenotypic heterogeneities. <i>Appl. Environ. Microbiol. 85(8)</i>: e02814-18. <a href=\"https://dx.doi.org/10.1128/aem.02814-18\" target=\"_blank\">https://dx.doi.org/10.1128/aem.02814-18</a>","AutID":499296,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321915,"RR":"<b>Nguyen, B.; Rubbens, P.; Kerckhof, F.-M.; Boon, N.; De Baets, B.; Waegeman, W.</b> (2019). Learning single‐cell distances from cytometry data. <i>Cytometry A 95(7)</i>: 782-791. <a href=\"https://dx.doi.org/10.1002/cyto.a.23792\" target=\"_blank\">https://dx.doi.org/10.1002/cyto.a.23792</a>","AutID":499296,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321916,"RR":"<b>Rubbens, P.; Schmidt, M.L.; Props, R.; Biddanda, B.A.; Boon, N.; Waegeman, W.; Denef, V.J.</b> (2019). Randomized Lasso links microbial taxa with aquatic functional groups inferred from flow cytometry. <i>mSystems 4(5)</i>: e00093-19. <a href=\"https://dx.doi.org/10.1128/msystems.00093-19\" target=\"_blank\">https://dx.doi.org/10.1128/msystems.00093-19</a>","AutID":499296,"MonDate":null,"AnaDate":2019,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":321914,"RR":"<b>García-Timermans, C.; Rubbens, P.; Kerckhof, F.-M.; Buysschaert, B.; Khalenkow, D.; Waegeman, W.; Skirtach, A.G.; Boon, N.</b> (2018). Label-free Raman characterization of bacteria calls for standardized procedures. <i>J. microbiol. methods 151</i>: 69-75. <a href=\"https://dx.doi.org/10.1016/j.mimet.2018.05.027\" target=\"_blank\">https://dx.doi.org/10.1016/j.mimet.2018.05.027</a>","AutID":499296,"MonDate":null,"AnaDate":2018,"PeerRev":1,"outputType":"1_A1","OpenAcc":0},{"BRefID":321911,"RR":"<b>Props, R.; Rubbens, P.; Besmer, M.; Buysschaert, B.; Sigrist, J.; Weilenmann, H.; Waegeman, W.; Boon, N.; Hammes, F.</b> (2018). Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data. <i>Wat. 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