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Identifying mixed Mycobacterium tuberculosis infections from whole genome sequence data.

Benjamin SobkowiakJudith R GlynnRein M G J HoubenKim MallardJody E PhelanJosé Afonso Guerra-AssunçãoLouis BandaThemba MzembeMiguel ViveirosRuth McNerneyJulian ParkhillAmelia C CrampinTaane G Clark
Published in: BMC genomics (2018)
Mixed Mycobacterium tuberculosis infection was identified in silico using whole genome sequence data. The methods presented here can be applied to population-wide analyses of tuberculosis to estimate the frequency of mixed infection, and to identify individual cases of mixed infections. These cases are important when considering the evolution and transmission of the disease, and in patient treatment.
Keyphrases
  • mycobacterium tuberculosis
  • pulmonary tuberculosis
  • electronic health record
  • big data
  • emergency department
  • machine learning
  • molecular docking
  • amino acid
  • artificial intelligence