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A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

R. Charlotte Eccleston, Emilia Manko, Susana Campino, Taane G. Clarke, Nicholas Furnham
doi: https://doi.org/10.1101/2022.01.25.477595
R. Charlotte Eccleston
aDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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  • For correspondence: charlotte.eccleston@lshtm.ac.uk
Emilia Manko
aDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Susana Campino
aDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Taane G. Clarke
aDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
bDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Nicholas Furnham
aDepartment of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Posted January 27, 2022.
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A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations
R. Charlotte Eccleston, Emilia Manko, Susana Campino, Taane G. Clarke, Nicholas Furnham
bioRxiv 2022.01.25.477595; doi: https://doi.org/10.1101/2022.01.25.477595
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A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations
R. Charlotte Eccleston, Emilia Manko, Susana Campino, Taane G. Clarke, Nicholas Furnham
bioRxiv 2022.01.25.477595; doi: https://doi.org/10.1101/2022.01.25.477595

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