Toward an Integrated Machine Learning Model of a Proteomics Experiment Article Swipe
Benjamin A. Neely
,
Viktoria Dorfer
,
Lennart Martens
,
Isabell Bludau
,
Robbin Bouwmeester
,
Sven Degroeve
,
Eric W. Deutsch
,
Siegfried Gessulat
,
Lukas Käll
,
Pawel Palczynski
,
Samuel Payne
,
Tobias Greisager Rehfeldt
,
Tobias Schmidt
,
Veit Schwämmle
,
Julian Uszkoreit
,
Juan Antonio Vizcaíno
,
Mathias Wilhelm
,
Magnus Palmblad
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jproteome.2c00711
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jproteome.2c00711
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jproteome.2c00711
- OA Status
- hybrid
- Cited By
- 60
- References
- 136
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319293599
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319293599Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acs.jproteome.2c00711Digital Object Identifier
- Title
-
Toward an Integrated Machine Learning Model of a Proteomics ExperimentWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-06Full publication date if available
- Authors
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Benjamin A. Neely, Viktoria Dorfer, Lennart Martens, Isabell Bludau, Robbin Bouwmeester, Sven Degroeve, Eric W. Deutsch, Siegfried Gessulat, Lukas Käll, Pawel Palczynski, Samuel Payne, Tobias Greisager Rehfeldt, Tobias Schmidt, Veit Schwämmle, Julian Uszkoreit, Juan Antonio Vizcaíno, Mathias Wilhelm, Magnus PalmbladList of authors in order
- Landing page
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https://doi.org/10.1021/acs.jproteome.2c00711Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1021/acs.jproteome.2c00711Direct OA link when available
- Concepts
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Proteomics, Computer science, Artificial intelligence, Machine learning, Computational biology, Chemistry, Biology, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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60Total citation count in OpenAlex
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2025: 18, 2024: 25, 2023: 15, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
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136Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_year | 2023 |
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