Login / Signup

Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis.

Tomatada IwamotoYoshiro MuraseShiomi YoshidaAkio AonoMakoto KurodaTsuyoshi SekizukaAkifumi YamashitaKengo KatoTakemasa TakiiKentaro ArikawaSeiya KatoSatoshi Mitarai
Published in: PloS one (2019)
Users can obtain more accurate predictions for PZA resistance than previously reported by manually checking the results and applying the 'non-wild type sequence' approach.
Keyphrases
  • mycobacterium tuberculosis
  • wild type
  • pulmonary tuberculosis
  • machine learning
  • social media
  • electronic health record
  • health information
  • healthcare
  • mass spectrometry
  • artificial intelligence