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Predicting antibiotic resistance de novo

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Fowler Lab
Fowler Lab

Predicting antibiotic resistance de novo

Preprints

Below are a list of manuscripts that are available as a preprint but have not yet been accepted in a peer-reviewed journal. Once accepted they will be moved to the Publications page.

7. Farrar A, Feehily C, Turner P, Zagajewski A, Chatzimichail S, Crook D, Andersson M, Oakley S, Barrett L, El Sayyed H, Fowler PW, Nellåker C, Kapanidis AN, Stoesser N
Infection Inspection: Using the power of citizen science to help with image-based prediction of antibiotic resistance in Escherichia coli
medRxiv preprint doi:10.1101/2023.12.11.23299807

6. Amoako D, Anh NT, Brouard M, Campeno Romero C, Castillo Ramirez A, Constantinides B, Crook DW et al.
SARS-CoV-2 sequencing with cloud-based analysis illustrates expedient co-ordinated surveillance of viral genomic epidemiology across six continents
medRxiv preprint doi:10.1101/2023.11.27.23298986

5. Brunner VM, Fowler PW
Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis.
bioRxiv preprint doi:10.1101/2023.06.21.545231

4. Constantinides B, Webster H, Gentry J, Bastable J, Dunn L, Oakley S, Swann J, Sanderson N, Fowler PW, Peto TEA, Stoesser N, Street T, Crook DW
Rapid turnaround multiplex sequencing of SARS-CoV-2: comparing tiling amplicon protocol performance
medRxiv preprint doi:10.1101/2021.12.28.21268461

2. The CRyPTIC Consortium
A generalisable approach to drug susceptibility prediction for M. tuberculosis using machine learning and whole- genome sequencing.
under review at Nature Medicine & bioRxiv preprint. doi: 10.1101/2021.09.14.458035

1. Carter JJ, Walker TM, Waker AS, Whitfield M, Morlock GP, Lynch CI, Adlard D, Peto TEA, Posey JE, Crook DW, Fowler PW.
Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches.
under review at JAC AMR & bioRxiv preprint doi:10.1101/518142

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