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Predicting antimicrobial resistance

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Fowler Lab
Fowler Lab

Predicting antimicrobial resistance

Category: clinical microbiology

antimicrobial resistance

AMyGDA now available from GitHub

Philip Fowler, 27th January 202027th January 2020

AMyGDA is a python module that analyses photographs of 96-well plates and, by examining each…

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antimicrobial resistance

New preprint: rapid prediction of AMR by free energy methods

Philip Fowler, 15th January 202015th January 2020

The story behind this preprint goes back to the workshop on free energy methods run…

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antimicrobial resistance

BashTheBug Coordinator post advertised

Philip Fowler, 15th November 2019

We are advertising for a Part-time Citizen Science Project Co-ordinator to come and work with…

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antimicrobial resistance

New preprint: Predicting pyrazinamide resistance by machine learning

Philip Fowler, 29th April 201929th April 2019

Usually, the protein that an antibiotic binds is essential for bacterial survival, which is how…

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antimicrobial resistance

GARC: A Grammar for Antimicrobial Resistance Catalogues

Philip Fowler, 25th November 201817th November 2020

During the CRyPTIC project it has become obvious that we need a grammar to describe…

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antimicrobial resistance

New publication: Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of M. tuberculosis

Philip Fowler, 26th October 2018

In this Microbiology paper we show how a Python package, called the Automated Mycobacterial Detection Growth…

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