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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 VALKENBORG, Dirk Barsnes, Harald Martens, Lennart |
Issue Date: | 2014 | Source: | 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. | Keywords: | bioinformatics; machine learning; pattern recognition; shotgun proteomics; standardization | Document URI: | http://hdl.handle.net/1942/16736 | ISSN: | 1615-9853 | e-ISSN: | 1615-9861 | DOI: | 10.1002/pmic.201300289 | ISI #: | 000332341200003 | Rights: | © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2015 |
Appears in Collections: | Research publications |
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