Andreas Hildebrandt

The paper uses a multi-property fine-tuned language model to predict lipid retention times from peptide data, improving accuracy in data-sparse scenarios.

The paper introduces midiaPASEF, a DIA scan mode that enhances information content in proteomics, enabling precise precursor m/z determination and efficient automated processing.

This paper demonstrates that locality-sensitive hashing can classify signals in high-throughput mass spectrometry data efficiently and scalably.