Toward an integrated machine Learning model of a proteomics experiment

Neely, B.A.; Dorfer, V.; Martens, L.; Bludau, I.; Bouwmeester, R.; Degroeve, S.; Deutsch, E.W.; Gessulat, S.; Kaell, L.; Palczynski, P.; Payne, S.H.; Rehfeldt, T.G.; Schmidt, T.; Schwaemmle, V.; Uszkoreit, J.; Vizcaino, J.A.; Wilhelm, M.; Palmblad, M.

doi:doi:10.1021/acs.jproteome.2c00711

Persistent URL of this record https://hdl.handle.net/1887/3590801

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Medicine / Leiden University Medical Centre (LUMC)

Neely, B.A.; Dorfer, V.; Martens, L.; Bludau, I.; Bouwmeester, R.; Degroeve, S.; ... ; Palmblad, M. (2023)

Toward an integrated machine Learning model of a proteomics experiment

Article / Letter to editor

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...Show moreIn 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.Show less

machine learning

deep learning

artificial intelligence

synthetic data

enzymatic digestion

liquid chromatography

ion mobility

tandem mass spectrometry

research integrity