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Machine learning applications in proteomics research: How the past can boost the future
Author(s) -
Kelchtermans Pieter,
Bittremieux Wout,
Grave Kurt,
Degroeve Sven,
Ramon Jan,
Laukens Kris,
Valkenborg Dirk,
Barsnes Harald,
Martens Lennart
Publication year - 2014
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201300289
Subject(s) - computer science , workflow , key (lock) , data science , field (mathematics) , artificial intelligence , economic shortage , machine learning , database , linguistics , philosophy , computer security , mathematics , government (linguistics) , pure mathematics
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.