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: Mar-2014
Publisher: WILEY-VCH Verlag
Series Title: Proteomics vol:14 issue:4-5 pages:353-368
Article number: early access
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.
ISSN: 1615-9853
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
Kelchtermans_et_al_ML_review_revision_1_submitted.pdfPreprint article text and figures Published 1816KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science