Peter Rubbens

ORCID iD: 0000-0001-5595-4758

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Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

Jasmine Heyse (UGent) , Florian Schattenberg, Peter Rubbens, Susann Müller, Willem Waegeman (UGent) , Nico Boon (UGent) and Ruben Props (UGent)

(2021) MSYSTEMS. 6(5).
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- Journal Article
- A1
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Computational analysis of microbial flow cytometry data

Peter Rubbens and Ruben Props (UGent)

(2021) MSYSTEMS. 6(1).
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- Journal Article
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PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure

Peter Rubbens, Ruben Props (UGent) , Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2021) MSPHERE. 6(1).
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- Journal Article
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Cytometric fingerprints of gut microbiota predict Crohn's disease state

Peter Rubbens, Ruben Props (UGent) , Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2021) ISME JOURNAL. 15(1). p.354-358
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- Journal Article
- A1
Discriminating bacterial phenotypes at the population and single‐cell level : a comparison of flow cytometry and Raman spectroscopy fingerprinting

Cristina García Timermans (UGent) , Peter Rubbens, Jasmine Heyse (UGent) , Frederiek-Maarten Kerckhof (UGent) , Ruben Props (UGent) , Andre Skirtach (UGent) , Willem Waegeman (UGent) and Nico Boon (UGent)

(2020) CYTOMETRY PART A. 97(7). p.713-726
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- Conference Paper
- C3
Fingerprinting microbial communities through flow cytometry and Raman spectroscopy

Cristina García Timermans (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Ruben Props (UGent) , Willem Waegeman (UGent) and Nico Boon (UGent)

(2019)
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- PhD Thesis
- open access
Machine learning approaches for microbial flow cytometry at the single-cell and community level

Peter Rubbens

(2019)
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- Journal Article
- A1
- open access
Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

Peter Rubbens, Marian L Schmidt, Ruben Props (UGent) , Bopaiah A Biddanda, Nico Boon (UGent) , Willem Waegeman (UGent) and Vincent J Denef

(2019) MSYSTEMS. 4(5).
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- Journal Article
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Learning single‐cell distances from cytometry data

Bac Nguyen Cong (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) , Bernard De Baets (UGent) and Willem Waegeman (UGent)

(2019) CYTOMETRY PART A. 95. p.782-791
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Cyt-Geist : current and future challenges in cytometry : reports of the CYTO 2018 conference workshops

Kamila Czechowska, Joanne Lannigan, Lili Wang, Judith Arcidiacono, Thomas M Ashhurst, Ruth M Barnard, Steven Bauer, Claudia Bispo, Diana L Bonilla, Ryan R Brinkman, et al.

(2019) CYTOMETRY PART A. 95A(6). p.598-644
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- Conference Paper
- C3
Fingerprinting microbial communities through flow cytometry and Raman spectroscopy

Cristina García Timermans (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Ruben Props (UGent) , Willem Waegeman (UGent) and Nico Boon (UGent)

(2019) BAGECO 15 : 15th symposium on bacterial genetics and ecology : ecosystem drivers in a changing planet. p.155-155
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- Journal Article
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Coculturing bacteria leads to reduced phenotypic heterogeneities

Jasmine Heyse (UGent) , Benjamin Buysschaert (UGent) , Ruben Props (UGent) , Peter Rubbens, Andre Skirtach (UGent) , Willem Waegeman (UGent) and Nico Boon (UGent)

(2019) APPLIED AND ENVIRONMENTAL MICROBIOLOGY. 85(8).
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- Conference Paper
- C3
Predicting microbial biodiversity using flow cytometry and 16S rRNA gene amplicon sequencing

Peter Rubbens, Ruben Props (UGent) , Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2019) COMMUNICATIONS IN AGRICULTURAL AND APPLIED BIOLOGICAL SCIENCES. 84(1). p.17-17
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- Conference Paper
- C1
A data-driven search for functional bacterial populations in aquatic systems using flow cytometry, 16S rRNA gene amplicon sequencing and the Randomized Lasso

Peter Rubbens, Marian Schmidt, Ruben Props (UGent) , Nico Boon (UGent) , Vincent Denef and Willem Waegeman (UGent)

(2018) FEED'18.
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- Journal Article
- A1
Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Ruben Props (UGent) , Peter Rubbens, Michael Besmer, Benjamin Buysschaert (UGent) , Jurg Sigrist, Hansueli Weilenmann, Willem Waegeman (UGent) , Nico Boon (UGent) and Frederik Hammes

(2018) WATER RESEARCH. 145. p.73-82
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- Conference Paper
- C3
Single-cell bacterial characterization using flow cytometry and Raman spectroscopy

Cristina García Timermans (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Ruben Props (UGent) , Willem Waegeman (UGent) and Nico Boon (UGent)

(2018) Belgian Society for Microbiology, Symposium abstracts.
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- Journal Article
- A1
Label-free Raman characterization of bacteria calls for standardized procedures

Cristina García Timermans (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Benjamin Buysschaert (UGent) , Dmitry Khalenkow, Willem Waegeman (UGent) , Andre Skirtach (UGent) and Nico Boon (UGent)

(2018) JOURNAL OF MICROBIOLOGICAL METHODS. 151. p.69-75
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- Journal Article
- A1
- open access
Stripping flow cytometry : how many detectors do we need for bacterial identification?

Peter Rubbens, Ruben Props (UGent) , Cristina García Timermans (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2017) CYTOMETRY PART A. 91(12). p.1184-1191
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- Conference Paper
- C3
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Detecting phenotypes with Raman spectroscopy

Cristina García Timermans (UGent) , Benjamin Buysschaert (UGent) , Peter Rubbens, Frederiek-Maarten Kerckhof (UGent) , Andre Skirtach (UGent) and Nico Boon (UGent)

(2017) FT-IR Spectroscopy in Microbiological and Medical Diagnostics, 11th Workshop, Abstracts.
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- Conference Paper
- C3
- open access
Identifying synthetic communities using flow cytometry and machine learning

Peter Rubbens, Cristina García Timermans (UGent) , Ruben Props (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2017) Microbial Resource Management, 2nd International symposium, Abstracts.
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- Journal Article
- A1
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Flow cytometric single-cell identification of populations in synthetic bacterial communities

Peter Rubbens, Ruben Props (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2017) PLOS ONE. 12(1).
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- Journal Article
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Absolute quantification of microbial taxon abundances

Ruben Props (UGent) , Frederiek-Maarten Kerckhof (UGent) , Peter Rubbens, Jo De Vrieze (UGent) , Emma Hernandez Sanabria (UGent) , Willem Waegeman (UGent) , Pieter Monsieurs, Frederik Hammes and Nico Boon (UGent)

(2017) ISME JOURNAL. 11(2). p.584-587
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- Conference Paper
- C1
- open access
Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Peter Rubbens, Willem Waegeman (UGent) , Ruben Props (UGent) and Nico Boon (UGent)

(2016) Machine Learning in Systems Biology, 10th International workshop, Papers.
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Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Peter Rubbens, Ruben Props (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)

(2016) 4th Workshop on machine learning in the life sciences. p.14-24
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- Conference Paper
- C1
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Clustering environmental flow cytometry data by searching density peaks

Peter Rubbens, Willem Waegeman (UGent) , Ruben Props (UGent) and Nico Boon (UGent)

(2016) BENELEARN 2016 : proceedings of the 25th Belgian-Dutch conference on machine learning.
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- Book Chapter
From theory choice to theory search: the essential tension between exploration and exploitation in science

Rogier De Langhe (UGent) and Peter Rubbens

(2015) Kuhn’s structure of scientific revolutions : 50 years on. In Boston Studies in the Philosophy and History of Science 311. p.105-114

Academic Bibliography

Peter Rubbens

Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting

Computational analysis of microbial flow cytometry data

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

Cytometric fingerprints of gut microbiota predict Crohn's disease state

Discriminating bacterial phenotypes at the population and single‐cell level : a comparison of flow cytometry and Raman spectroscopy fingerprinting

Fingerprinting microbial communities through flow cytometry and Raman spectroscopy

Machine learning approaches for microbial flow cytometry at the single-cell and community level

Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry

Learning single‐cell distances from cytometry data

Cyt-Geist : current and future challenges in cytometry : reports of the CYTO 2018 conference workshops

Fingerprinting microbial communities through flow cytometry and Raman spectroscopy

Coculturing bacteria leads to reduced phenotypic heterogeneities

Predicting microbial biodiversity using flow cytometry and 16S rRNA gene amplicon sequencing

A data-driven search for functional bacterial populations in aquatic systems using flow cytometry, 16S rRNA gene amplicon sequencing and the Randomized Lasso

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data

Single-cell bacterial characterization using flow cytometry and Raman spectroscopy

Label-free Raman characterization of bacteria calls for standardized procedures

Stripping flow cytometry : how many detectors do we need for bacterial identification?

Detecting phenotypes with Raman spectroscopy

Identifying synthetic communities using flow cytometry and machine learning

Flow cytometric single-cell identification of populations in synthetic bacterial communities

Absolute quantification of microbial taxon abundances

Identifying synthetic microbial communities by learning in silico communities using flow cytometry

Learning in silico communities to perform flow cytometric identification of synthetic bacterial communities

Clustering environmental flow cytometry data by searching density peaks

From theory choice to theory search: the essential tension between exploration and exploitation in science

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