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Machine learning approach for the prediction of the number of Sulphur atoms in peptides using the theoretical aggregated isotope distribution.

Annelies AgtenJürgen ClaesenTomasz BurzykowskiDirk Valkenborg
Published in: Rapid communications in mass spectrometry : RCM (2023)
Based on the validation analysis it can be concluded that the prediction of the number of Sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher order isotope peaks more accurately.
Keyphrases
  • machine learning
  • gas chromatography
  • mass spectrometry
  • artificial intelligence
  • magnetic resonance imaging
  • current status
  • magnetic resonance
  • computed tomography
  • simultaneous determination