Machine learning to predict the source of campylobacteriosis using whole genome data

Nicolas Arning, Samuel K. Sheppard, Sion Bayliss, university Oxford, Daniel J. Wilson

Research output: Contribution to journal › Article (Academic Journal) › peer-review

45 Citations (Scopus)

62 Downloads (Pure)

Access to Document

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via PLOS at https://doi.org/10.1371/journal.pgen.1009436 . Please refer to any applicable terms of use of the publisher.
Final published version, 2.35 MBLicence: CC BY

@article{27d6db7e5da744da9746bcdf156442fd,

title = "Machine learning to predict the source of campylobacteriosis using whole genome data",

author = "Nicolas Arning and Sheppard, \{Samuel K.\} and Sion Bayliss and university Oxford and Wilson, \{Daniel J.\}",

year = "2021",

month = oct,

day = "18",

doi = "10.1371/journal.pgen.1009436",

language = "English",

journal = "PLOS Genetics",

issn = "1553-7390",

publisher = "Public Library of Science",

}

TY - JOUR

T1 - Machine learning to predict the source of campylobacteriosis using whole genome data

AU - Arning, Nicolas

AU - Sheppard, Samuel K.

AU - Bayliss, Sion

AU - Oxford, university

AU - Wilson, Daniel J.

PY - 2021/10/18

Y1 - 2021/10/18

U2 - 10.1371/journal.pgen.1009436

DO - 10.1371/journal.pgen.1009436

M3 - Article (Academic Journal)

C2 - 34662334

SN - 1553-7390

JO - PLOS Genetics

JF - PLOS Genetics

ER -