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Machine learning applications in proteomics research: How the past can boost the future

Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, S; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/1956/16943
Date
2014
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  • Department of Biomedicine [447]
Original version
https://doi.org/10.1002/pmic.201300289
Abstract
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.
Journal
Proteomics
Copyright
Copyright 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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