Publication
Title
Machine learning applications in proteomics research : how the past can boost the future
Author
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 is available to train and subsequently evaluate an algorithm on. Since mass spectrometry based proteomics has no shortage of complex problems, and since publicly available data is 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.
Language
English
Source (journal)
Proteomics. - Weinheim
Publication
Weinheim : 2014
ISSN
1615-9853
Volume/pages
14:4-5(2014), p. 353-366
ISI
000332341200003
Full text (Publishers DOI)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 10.12.2013
Last edited 17.05.2017
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