dc.contributor.author | Kelchtermans, Pieter | en_US |
dc.contributor.author | Bittremieux, Wout | en_US |
dc.contributor.author | De Grave, Kurt | en_US |
dc.contributor.author | Degroeve, S | en_US |
dc.contributor.author | Ramon, Jan | en_US |
dc.contributor.author | Laukens, Kris | en_US |
dc.contributor.author | Valkenborg, Dirk | en_US |
dc.contributor.author | Barsnes, Harald | en_US |
dc.contributor.author | Martens, Lennart | en_US |
dc.date.accessioned | 2017-11-20T08:51:02Z | |
dc.date.available | 2017-11-20T08:51:02Z | |
dc.date.issued | 2014 | |
dc.Published | Kelchtermans, Bittremieux, De Grave, Degroeve S, Ramon, Laukens K, Valkenborg, Barsnes H, Martens L. Machine learning applications in proteomics research: How the past can boost the future. Proteomics. 2014;14(4-5):353-366 | eng |
dc.identifier.issn | 1615-9861 | |
dc.identifier.issn | 1615-9853 | |
dc.identifier.uri | https://hdl.handle.net/1956/16943 | |
dc.description.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. | en_US |
dc.language.iso | eng | eng |
dc.rights | All rights reserved. | eng |
dc.subject | Bioinformatics | eng |
dc.subject | Machine learning | eng |
dc.subject | Pattern recognition | eng |
dc.subject | Shotgun proteomics | eng |
dc.subject | Standardization | eng |
dc.title | Machine learning applications in proteomics research: How the past can boost the future | en_US |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.date.updated | 2017-09-06T10:42:49Z | |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | Copyright 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. | |
dc.identifier.doi | https://doi.org/10.1002/pmic.201300289 | |
dc.identifier.cristin | 1111047 | |
dc.source.journal | Proteomics | |
dc.source.40 | 14 | |
dc.source.14 | 4-5 | |
dc.source.pagenumber | 353-366 | |
dc.relation.project | Norges forskningsråd: 204833 | |
dc.subject.nsi | VDP::Medisinske fag: 700::Basale medisinske, odontologiske og veterinærmedisinske fag: 710::Medisinsk biokjemi: 726 | |
dc.subject.nsi | VDP::Midical sciences: 700::Basic medical, dental and veterinary sciences: 710::Medical biochemistry: 726 | |
dc.subject.nsi | VDP::Medisinske Fag: 700 | en_US |