Robin Schmid

The paper develops a drug exposure resource from untargeted metabolomics data to identify medications without clinical records.

The paper curates LC-MS/MS data from plant extracts to create a taxonomically focused database for exploring plant-derived molecules.

MS2DeepScore 2.0 predicts chemical similarity between tandem mass spectra across ionization modes, improving metabolite annotation and data exploration in untargeted metabolomics.

MicrobeMASST identifies microbial-derived metabolites and their producers using taxonomically-informed mass spectrometry data without prior knowledge.

The paper creates a suspect spectral library for untargeted metabolomics by propagating annotations from public MS/MS spectra to improve interpretation of unannotated spectra.

The paper develops a functional metabolomics strategy to annotate metabolites in the human scalp and identifies the metabolic niche of Staphylococcus epidermidis.

The paper compares cosine, modified cosine, and neutral loss methods for MS/MS spectrum alignment to find structurally related molecules, finding modified cosine performs best.

MassQL, an open-source query language, lets users search raw mass spectrometry data from any vendor for isotope, fragmentation and neutral-loss patterns, from one file to entire public repositories.

The paper introduces Ion Identity Molecular Networking to connect ion species of the same molecule, improving network connectivity and annotation.

The paper introduces a web-based tool for collaborative analysis of mass spectrometry data, enabling remote inspection, visualization, and analysis of private and public datasets.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.