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Proteomics. 2014 Mar;14(4-5):353-66. doi: 10.1002/pmic.201300289. Epub 2014 Jan 21.

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

Author information

1
Department of Medical Protein Research, VIB, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium; Flemish Institute for Technological Research (VITO), Boeretang, Mol, Belgium.

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

PMID:
24323524
DOI:
10.1002/pmic.201300289
[Indexed for MEDLINE]

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