Nature Communications (May 2020)

A biochemically-interpretable machine learning classifier for microbial GWAS

  • Erol S. Kavvas,
  • Laurence Yang,
  • Jonathan M. Monk,
  • David Heckmann,
  • Bernhard O. Palsson

DOI
https://doi.org/10.1038/s41467-020-16310-9
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 11

Abstract

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Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.