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Toward an Integrated Machine Learning Model of a Proteomics Experiment

Published

Author(s)

Ben Neely, Viktoria Dorfer, Lennart Martens, Isabell Bludau, Robbin Bouwmeester, Sven Degroeve, Eric Deutsch, Siegfried Gessulat, Tobias Rehfeldt, Lukas Käll, Veit Schwämmle, Samuel Payne, Tobias Schmidt, Pawel Palczynski, Julian Uszkoreit, Juan Antonio Vizcaino, Mathias Wilhelm, Magnus Palmblad

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.
Citation
ACS Journal of Proteome Research

Keywords

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

Citation

Neely, B. , Dorfer, V. , Martens, L. , Bludau, I. , Bouwmeester, R. , Degroeve, S. , Deutsch, E. , Gessulat, S. , Rehfeldt, T. , Käll, L. , Schwämmle, V. , Payne, S. , Schmidt, T. , Palczynski, P. , Uszkoreit, J. , Vizcaino, J. , Wilhelm, M. and Palmblad, M. (2023), Toward an Integrated Machine Learning Model of a Proteomics Experiment, ACS Journal of Proteome Research, [online], https://doi.org/10.1021/acs.jproteome.2c00711, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935708 (Accessed May 21, 2024)

Issues

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Created February 6, 2023, Updated February 24, 2023