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Machine learning approaches to predict the plant-associated phenotype of Xanthomonas strains
View ORCID ProfileDennie te Molder, View ORCID ProfileWasin Poncheewin, View ORCID ProfilePeter J. Schaap, View ORCID ProfileJasper J. Koehorst
doi: https://doi.org/10.1101/2021.05.25.445602
Dennie te Molder
1Laboratory of Systems and Synthetic Biology | Department of Agrotechnology and Food Sciences, Wageningen University and Research, Wageningen, the Netherlands
Wasin Poncheewin
1Laboratory of Systems and Synthetic Biology | Department of Agrotechnology and Food Sciences, Wageningen University and Research, Wageningen, the Netherlands
Peter J. Schaap
1Laboratory of Systems and Synthetic Biology | Department of Agrotechnology and Food Sciences, Wageningen University and Research, Wageningen, the Netherlands
2UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University and Research, Wageningen, the Netherlands
Jasper J. Koehorst
1Laboratory of Systems and Synthetic Biology | Department of Agrotechnology and Food Sciences, Wageningen University and Research, Wageningen, the Netherlands
2UNLOCK Large Scale Infrastructure for Microbial Communities, Wageningen University and Research, Wageningen, the Netherlands
Posted May 25, 2021.
Machine learning approaches to predict the plant-associated phenotype of Xanthomonas strains
Dennie te Molder, Wasin Poncheewin, Peter J. Schaap, Jasper J. Koehorst
bioRxiv 2021.05.25.445602; doi: https://doi.org/10.1101/2021.05.25.445602
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