A biochemically-interpretable machine learning classifier for microbial GWAS - Erol S. Kavvas; Laurence Yang; Jonathan M. Monk; David Heckmann; Bernhard O. Palsson (Nature Communications, 2020)
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.