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A biochemically-interpretable machine learning classifier for microbial GWAS
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DOI：10.1038/s41467-020-16310-9.png)
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摘要
En 中文
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC=0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results. 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.
Keyword:
PARA-AMINOSALICYLIC ACID
MYCOBACTERIUM-TUBERCULOSIS
DRUG-RESISTANCE
MUTATIONS
MODELS
PYRAZINAMIDE
METABOLISM
VIRULENCE
PATHWAYS
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
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