Gary Siuzdak

Siuzdak argues the field must correct AI-fueled “phantom metabolites” before trusting predictive models, favoring experimentally grounded measurement.

With 960,000 empirically acquired standards, Siuzdak shows how METLIN delivers reliable, cross-instrument IDs and reveals fragmentation artifacts.

As metabolomics datasets balloon, Siuzdak explains using retention time and in-source fragmentation checks to separate real molecules from artifacts.

Siuzdak separates true standard-based empirical spectra from artifactual signals, warning AI trained on flawed data risks perpetuating errors.

Gary Siuzdak explains how uMRM converts inconsistent untargeted LC–MS data into standardized MRM transitions.

Metabolomics, the study of small molecule metabolites that are found within a biological sample, is an emerging field of study. Progress in this field depends upon technological advancement in the fields of LC–MS and separation technology.