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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
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  • For correspondence: nicolas.arning@bdi.ox.ac.uk
Samuel K. Sheppard
2The Milner Centre of Evolution, Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AZ, UK
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David A. Clifton
3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, OX3 7DQ, UK
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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.
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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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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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