Journal
PROTEOMICS
Volume 14, Issue 4-5, Pages 353-366Publisher
WILEY
DOI: 10.1002/pmic.201300289
Keywords
Bioinformatics; Machine learning; Pattern recognition; Shotgun proteomics; Standardization
Funding
- SBO grant InSPECtor of the Flemish agency for Innovation by Science and Technology (IWT) [120025]
- Research Council of Norway
- Ghent University (Multidisciplinary Research Partnership Bioinformatics: from nucleotides to networks)
- PRIME-XS project [262067]
- ProteomeXchange project [260558]
- European Union
- ERC [240186]
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Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
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