antimicrobial resistance New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network Philip Fowler, 29th October 202530th October 2025 In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict… Continue Reading
New paper: Evaluating 12 WGS analysis pipelines for MBTC Philip Fowler, 21st October 202529th October 2025 Ruan Spies did a careful systematic analysis of the publicly-available pipelines that claimed to process… Continue Reading
antimicrobial resistance New paper: What can subpopulations tell us about rifampicin resistance? Philip Fowler, 14th October 202514th October 2025 Last Thursday this work which we’d previously preprinted looking at looking at rifampicin-resistant subpopulations in… Continue Reading
antimicrobial resistance New preprint: rapidly and reproducibly building resistant catalogues for M. tuberculosis Philip Fowler, 3rd October 202530th October 2025 The CRyPTIC project carried out many exciting research projects but it never quite got around… Continue Reading
antimicrobial resistance New paper: predicting rifampicin resistance via free energy simulation Philip Fowler, 23rd September 20253rd October 2025 This work was carried out by Xibei Zhang, who is doing her PhD with Peter… Continue Reading
antimicrobial resistance CRyPTIC datasets available through new website Philip Fowler, 25th June 20257th July 2025 The CRyPTIC project ran from 2016 to 2022 and collected >20,000 clinical samples from patients… Continue Reading