AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Lauren M. Petrick*, Noam Shomron

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Original languageEnglish
Article number100978
JournalCell Reports Physical Science
Issue number7
StatePublished - 20 Jul 2022


FundersFunder number
National Cancer InstituteUH2CA248974
National Institute of Environmental Health SciencesR21ES030882, P30ES023515, U2CES030859, U2CES026561, R01ES031117


    • artificial intelligence
    • biomarker
    • biomedical
    • endogenous
    • exogenous
    • exposomics
    • high-resolution mass spectrometry
    • machine learning
    • processing
    • untargeted metabolomics


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