Sebastian Böcker

The paper uses chemistry-based vectors to analyze untargeted mass spectrometry data and identifies compound classes that differentiate natural biomes.

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.

BERTax uses deep learning to classify DNA sequences taxonomically without requiring known database relatives, outperforming existing methods for novel organisms and combining with databases to improve accuracy.

MSNovelist generates small molecule structures de novo from mass spectra, outperforming database-based methods for novel compounds.

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.

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

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.

The paper develops methods to estimate false discovery rates in untargeted metabolomics annotations and shows how adjusting spectral matching parameters can significantly increase the number of annotations.