Search results (143)
« Back to PublicationsPredicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network
Journal article
Dissanayake D. et al, (2026), BMC Microbiology, 26
Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny
Journal article
Hunt M. et al, (2026), Nature Methods, 23, 653 - 662
Evaluation of an Oxford Nanopore sequencing workflow for mycobacteria from primary MGIT culture
Preprint
Baker CS. et al, (2026)
Characterizing the performance of an antibiotic resistance prediction tool, gnomonicus, using a diverse test set of 2,663 Mycobacterium tuberculosis samples
Journal article
Westhead J. et al, (2025), Microbial Genomics, 11
Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study
Journal article
Spies R. et al, (2025), The Lancet Microbe, 6, 101210 - 101210
Enhancement of midwives' and child healthcare nurses' attitudes towards the WHO's recommendations following breastfeeding training: A quasi-experimental study.
Journal article
Blixt I. et al, (2025), Sexual & reproductive healthcare : official journal of the Swedish Association of Midwives, 46
Rapid, accurate, and reproducible de novo prediction of resistance to antituberculars
Journal article
Zhang X. et al, (2025), mSphere, 10
Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network
Preprint
Dissanayake D. et al, (2025)
Rapidly and reproducibly building a comprehensive catalogue of resistance-associated variants for M. tuberculosis
Preprint
Adlard D. et al, (2025)
Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired
Journal article
Brunner VM. and Fowler PW., (2025), JAC-Antimicrobial Resistance, 7
An improved catalogue for whole-genome sequencing prediction of bedaquiline resistance in Mycobacterium tuberculosis using a reproducible algorithmic approach
Journal article
Adlard D. et al, (2025), Microbial Genomics, 11
Predicting rifampicin resistance in Mycobacterium tuberculosis using machine learning informed by protein structural and chemical features
Journal article
Lynch CI. et al, (2025), ERJ Open Research, 11, 00952 - 2024
Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired
Preprint
Brunner VM. and Fowler PW., (2025)
Deep Learning-based Framework for Mycobacterium Tuberculosis Bacterial Growth Detection for Antimicrobial Susceptibility Testing
Preprint
Vo H-AT. et al, (2025)