TL;DR
Overall, this chapter aims to empower biologists and chemists to harness machine learning for metabolomics, transforming unannotated spectra into interpretable chemical knowledge.
Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics has become a central approach for characterizing the chemical diversity of biological systems, particularly in natural product discovery. However, the scale and complexity of untargeted LC-MS/MS datasets present a persistent challenge: only a...
Mass spectrometry-based metabolomics is essential for deciphering human metabolic functions and metabolite dark matter. However, the dark matter of the human metabolome is intricately hidden. Therefore, we developed MassBank Human, a curated library that distills 36,512,540 MS2 spectra from 76 public human datasets int...
Modern mass spectrometry (MS) experiments generate increasingly complex, multidimensional datasets across diverse analytical modalities, including liquid chromatography (LC)-MS, gas chromatography (GC)-MS, ion mobility spectrometry, and mass spectrometry imaging. Efficient analysis of such heterogeneous data has tradit...
This work demonstrates that a pretrained spectrum encoder for tandem mass spectrometry proteomics trained on de novo sequencing learns generalizable representations of spectra, improves performance on downstream tasks where training data is limited, and can ultimately enhance data acquisition and analysis in proteomics...
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