A biochemically-interpretable machine learning classifier for microbial GWAS - Erol S. Kavvas (Nature Portfolio, 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.