Marcus Ludwig

The paper evaluates coverage bias in small molecule datasets and proposes methods to assess and improve dataset representativeness for machine learning models.

The paper introduces a novel method that achieves 97.6% correct annotation of small molecule tandem mass spectra by combining spectral data with structural database information.

COSMIC assigns confidence to structural annotations from mass spectra by combining database generation, in silico annotation, and a confidence score, outperforming existing methods and annotating novel structures.

CANOPUS is a tool that predicts compound classes from fragmentation spectra, enabling annotation of unknown compounds without reference data.

The paper introduces Qemistree, a method for analyzing metabolomics mass spectrometry data using hierarchical molecular fingerprints and chemical ontologies to explore chemical composition.

ZODIAC improves molecular formula annotation in metabolomics by combining SIRIUS results with Gibbs sampling, reducing errors and discovering novel compounds.

The paper introduces FBMN, a method for improving molecular networking by enhancing quantitative analysis and isomer distinction using feature detection and alignment tools in the GNPS platform.