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Machine learning to predict the source of campylobacteriosis using whole genome data
View ORCID ProfileNicolas Arning, View ORCID ProfileSamuel K. Sheppard, David A. Clifton, Daniel J. Wilson
doi: https://doi.org/10.1101/2021.02.23.432443
Nicolas Arning
1Big Data institute, Nuffield Department of Population Health, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK
Samuel K. Sheppard
2The Milner Centre of Evolution, Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AZ, UK
David A. Clifton
3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, UK
Daniel J. Wilson
1Big Data institute, Nuffield Department of Population Health, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford, OX3 7LF, UK

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Posted February 23, 2021.
Machine learning to predict the source of campylobacteriosis using whole genome data
Nicolas Arning, Samuel K. Sheppard, David A. Clifton, Daniel J. Wilson
bioRxiv 2021.02.23.432443; doi: https://doi.org/10.1101/2021.02.23.432443
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