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Search results (137)

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Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network.

Dissanayake D. et al, (2026), BMC microbiology

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

Hunt M. et al, (2026), Nature Methods

Evaluation of an Oxford Nanopore sequencing workflow for mycobacteria from primary MGIT culture

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

Westhead J. et al, (2025), Microbial Genomics, 11

Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study

Spies R. et al, (2025), The Lancet Microbe, 6, 101210 - 101210

Rapid, accurate, and reproducible de novo prediction of resistance to antituberculars

Zhang X. et al, (2025), mSphere, 10

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

Dissanayake D. et al, (2025)

Rapidly and reproducibly building a comprehensive catalogue of resistance-associated variants for M. tuberculosis

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

Brunner VM. and Fowler PW., (2025), JAC-Antimicrobial Resistance, 7

n improved catalogue for whole-genome sequencing prediction of bedaquiline resistance in Mycobacterium tuberculosis using a reproducible algorithmic approach

Adlard D. et al, (2025), Microbial Genomics, 11

Predicting rifampicin resistance in Mycobacterium tuberculosis using machine learning informed by protein structural and chemical features

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

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)

n improved catalogue for whole-genome sequencing prediction of bedaquiline resistance in M. tuberculosis using a reproducible algorithmic approach

Adlard D. et al, (2025)

Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing

Vo H-AT. et al, (2025), Computational and Structural Biotechnology Journal, 27, 2208 - 2218

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