Toward an Integrated Machine Learning Model of a Proteomics Experiment

Benjamin A. Neely, Viktoria Dorfer, Lennart Martens, Isabell Bludau, Robbin Bouwmeester, Sven Degroeve, Eric W. Deutsch, Siegfried Gessulat, Lukas Käll, Pawel Palczynski, Samuel H. Payne, Tobias Greisager Rehfeldt, Tobias Schmidt, Veit Schwämmle, Julian Uszkoreit, Juan Antonio Vizcaíno, Mathias Wilhelm, Magnus Palmblad

Research output: Contribution to journalReview articlepeer-review

12 Scopus citations

Abstract

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Original languageEnglish
Pages (from-to)681-696
Number of pages16
JournalJournal of Proteome Research
Volume22
Issue number3
DOIs
StatePublished - 3 Mar 2023

Keywords

  • artificial intelligence
  • deep learning
  • enzymatic digestion
  • ion mobility
  • liquid chromatography
  • machine learning
  • research integrity
  • synthetic data
  • tandem mass spectrometry

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