<p>Antimicrobial resistance prediction from whole-genome sequence data using transfer learning</p>

Logan Kopas; Anthony Kusalik; Dave  Schneider

doi:10.7490/f1000research.1117257.1

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Kopas L, Kusalik A and Schneider D. Antimicrobial resistance prediction from whole-genome sequence data using transfer learning [version 1; not peer reviewed]. F1000Research 2019, 8:1333 (poster) (https://doi.org/10.7490/f1000research.1117257.1)

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Antimicrobial resistance prediction from whole-genome sequence data using transfer learning

Logan Kopas¹, Anthony Kusalik, Dave Schneider

Published 01 Aug 2019 (https://doi.org/10.7490/f1000research.1117257.1)

Author Affiliations

¹ University of Saskatchewan, Canada

Copyright © 2019 Kopas L et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 01 Aug 2019

Antimicrobial resistance prediction from whole-genome sequence data using transfer learning
[version 1; not peer reviewed]

Logan Kopas¹, Anthony Kusalik, Dave Schneider

Author Affiliations

¹ University of Saskatchewan, Canada

Presented at

ISMB/ECCB 2019

Abstract

Competing Interests

No competing interests were disclosed

Keywords

transfer learning, deep learning, AMR, prediction, phenotype prediction, Neisseria gonorrhoeae, Lens culinaris, SNP, WGS, genetic variant, antimicrobial resistance, CNN, convolutional neural network

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