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Publications

Subpopulations in clinical samples ofM. tuberculosiscan give rise to rifampicin resistance and shed light on how resistance is acquired

Brunner VM. and Fowler PW., (2025)

Deep Learning-based Framework for Mycobacterium Tuberculosis Bacterial Growth Detection for Antimicrobial Susceptibility Testing

Vo H-AT. et al, (2025)

An improved catalogue for whole-genome sequencing prediction of bedaquiline resistance inM. tuberculosisusing a reproducible algorithmic approach

Adlard D. et al, (2025)

Predicting rifampicin resistance inM. tuberculosisusing machine learning informed by protein structural and chemical features

Lynch CI. et al, (2025), ERJ Open Research, 00952 - 2024

Enhancement and validation of the antibiotic resistance prediction performance of a cloud-based genetics processing platform for Mycobacteria

Westhead J. et al, (2024)

Infection Inspection: using the power of citizen science for image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin

Farrar A. et al, (2024), Scientific Reports, 14

Predicting rifampicin resistance inM. tuberculosisusing machine learning informed by protein structural and chemical features

Lynch CI. et al, (2024)

Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning

(2024), PLOS Computational Biology, 20, e1012260 - e1012260

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny

Hunt M. et al, (2024)

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches

Carter JJ. et al, (2024), JAC-Antimicrobial Resistance, 6

Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis

Brunner VM. and Fowler PW., (2024), Microbial Genomics, 10

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

Barilar I. et al, (2024), Nature Communications, 15

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli

Davies TJ. et al, (2023), Microbial Genomics, 9

A validated cloud-based genomic platform for co-ordinated, expedient global analysis of SARS-CoV-2 genomic epidemiology

Amoako D. et al, (2023)

Compensatory mutations are associated with increasedin vitrogrowth in resistant clinical samples ofMycobacterium tuberculosis

Brunner V. and Fowler PW., (2023)

Reply: Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis

(2023), European Respiratory Journal, 61, 2300426 - 2300426

Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance inMycobacterium tuberculosis

Brankin AE. and Fowler PW., (2023), JAC-Antimicrobial Resistance, 5

Predicting antibiotic resistance in complex protein targets using alchemical free energy methods

Brankin AE. and Fowler PW., (2022), Journal of Computational Chemistry, 43, 1771 - 1782

Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofM. tuberculosis

(2022), European Respiratory Journal, 60, 2200239 - 2200239

High fluoroquinolone resistance proportions among multidrug-resistant tuberculosis driven by dominant L2 Mycobacterium tuberculosis clones in the Mumbai Metropolitan Region

Dreyer V. et al, (2022), Genome Medicine, 14

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