Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data

Scientific Reports

Volumn 9, Issue 1, 2019, Pages

  Obolski, Uri ^a   Gori, Andrea ^b   Lourenço, José ^a   Thompson, Craig ^a   Thompson, Robin ^a   French, Neil ^c   Heyderman, Robert S ^b   Gupta, Sunetra ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c LIVERPOOL SCHOOL OF TROPICAL MEDICINE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL GENE; BACTERIAL GENOME; CEREBROSPINAL FLUID; GENETICS; HUMAN; MENINGITIS; MICROBIOLOGY; PATHOGENICITY; PNEUMONIA; SEPSIS; STREPTOCOCCUS PNEUMONIAE; WHOLE GENOME SEQUENCING;

GENES, BACTERIAL; GENOME, BACTERIAL; HUMANS; MENINGITIS; PNEUMONIA; SEPSIS; STREPTOCOCCUS PNEUMONIAE; WHOLE GENOME SEQUENCING;

EID: 85062766160     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-019-40346-7     Document Type: Article

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