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Predicting antimicrobial resistance

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Fowler Lab
Fowler Lab

Predicting antimicrobial resistance

Category: tuberculosis

New preprint: a deep learning model that can read 96-well broth micro dilution plates

Philip Fowler, 23rd February 202523rd February 2025

The CRyPTIC project used bespoke 96-well broth microdilution plates to measure the minimum inhibitory concentrations…

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New preprint: comparing different genetics analysis pipelines for tuberculosis

Philip Fowler, 13th January 202513th January 2025

Ruan Spies has done a careful systematic comparison of the current genetics pipelines that purport…

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New preprint: validating antibiotic resistance prediction in our Myco pipeline

Philip Fowler, 9th November 202413th January 2025

Over the last 18 months or so we’ve been designing, coding and testing a Mycobacterial…

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antimicrobial resistance

New preprint: predicting rifampicin resistance

Philip Fowler, 16th August 202416th August 2024

In this preprint we train a series of machine learning models on protein mutations found…

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antimicrobial resistance

New paper: Quantitative drug susceptibility testing for M. tuberculosis using unassembled sequencing data and machine learning

Philip Fowler, 14th August 202414th August 2024

This is the last paper from the initial set of CRyPTIC publications following the project’s…

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antimicrobial resistance

Updating the Grammar for Antimicrobial Resistance Catalogues

Philip Fowler, 18th July 202418th July 2024

This blog updates an old (and now out of date) post describing the grammar we’ve…

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