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PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes inmicrobial community structure
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
In: mSphere. American Society for Microbiology: Washington. e-ISSN 2379-5042
Peer reviewed article  
Available in  Authors 
Author keywords
    diversity, fingerprint, flow cytometry, machine learning, microbial communities, mixture model
Authors  Top 
  • Rubbens, P.
  • Props, R.
  • Kerckhof, F.-M.
  • Boon, N.
  • Waegeman, W.
All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors 
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