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Fowler Lab

Predicting antimicrobial resistance

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Fowler Lab
Fowler Lab

Predicting antimicrobial resistance

Welcome

The main aim of our research is to develop methods that can de novo predict the effect of protein mutations on the action of antibiotics.

Latest News

  • New preprint: using genetics to analyse ESBL infections in a neonatal ICU ward7th July 2026
  • Fourth Dx4LMICs conference7th July 2026
  • ESM Annual Congress 202626th June 2026
  • Oxford Public & Community Engagement Conference12th June 2026
  • NIHR PPI In Action Webinar on BashTheBug1st June 2026
  • New preprint: ONT sequencing is comparable to Illumina sequencing for M. tuberculosis9th April 2026
  • Congratulations Dr Adlard!7th March 2026
  • New paper: predicting pyrazinamide using a graph convolutional network3rd March 2026
  • New paper: Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny9th February 2026
  • New preprint: evaluating a laboratory workflow for sequencing Mycobacteria using long-read sequencing7th February 2026
  • Gr-ADI consortium announced22nd January 2026
  • New paper: how well can we predict AMR in tuberculosis samples?16th December 2025
  • Congratulations Dr Brunner!10th December 2025
  • New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network29th October 2025
  • New paper: Evaluating 12 WGS analysis pipelines for MBTC21st October 2025
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