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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16736

Title: Machine learning applications in proteomics research: How the past can boost the future
Authors: Kelchtermans, Pieter
Bittremieux, Wout
De Grave, Kurt
Degroeve, Sven
Ramon, Jan
Laukens, Kris
Barsnes, Harald
Martens, Lennart
Issue Date: 2014
Citation: PROTEOMICS, 14 (4-5), p. 353-366
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.
URI: http://hdl.handle.net/1942/16736
DOI: 10.1002/pmic.201300289
ISI #: 000332341200003
ISSN: 1615-9853
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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