PhenoGMM: Gaussian mixture modeling of cytometry data quantifies changes inmicrobial community structure

Rubbens, P.; Props, R.; Kerckhof, F.-M.; Boon, N.; Waegeman, W.

doi:/10.1128/msphere.00530-20

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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, more

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VLIZ: Open access 370898 [ download pdf ]

Author keywords

diversity, fingerprint, flow cytometry, machine learning, microbial communities, mixture model

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Rubbens, P., more Props, R., more Kerckhof, F.-M., more	Boon, N., more Waegeman, W., more

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