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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: validating antibiotic resistance prediction in our Myco pipeline9th November 2024
• New paper: Infection Inspection10th September 2024
• New preprint: predicting rifampicin resistance16th August 2024
• New paper: Quantitative drug susceptibility testing for M. tuberculosis using unassembled sequencing data and machine learning14th August 2024
• Updating the Grammar for Antimicrobial Resistance Catalogues18th July 2024
• Dx4LMICs conference3rd July 2024
• FowlerLab at ESM 20241st July 2024
• Kafka and tuberculosis21st June 2024
• New preprint: processing 3.9 million SARS-CoV-2 samples to make a consistent phylogenetic tree7th May 2024
• New paper: predicting pyrazinamide resistance20th March 2024
• Fitness compensation in M. tuberculosis: A game of genetic chess11th March 2024
• New paper: detecting compensatory mutations in the RNAP of M. tuberculosis5th February 2024
• New preprint: processing SARS-CoV-2 genetics in the cloud31st January 2024
• New paper: quantitative measurement of effect of mutations on antibiotics in M. tuberculosis15th January 2024
• Our World in Data10th January 2024

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