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http://hdl.handle.net/1942/16736
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DC Field | Value | Language |
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dc.contributor.author | Kelchtermans, Pieter | - |
dc.contributor.author | Bittremieux, Wout | - |
dc.contributor.author | De Grave, Kurt | - |
dc.contributor.author | Degroeve, Sven | - |
dc.contributor.author | Ramon, Jan | - |
dc.contributor.author | Laukens, Kris | - |
dc.contributor.author | VALKENBORG, Dirk | - |
dc.contributor.author | Barsnes, Harald | - |
dc.contributor.author | Martens, Lennart | - |
dc.date.accessioned | 2014-04-29T13:41:52Z | - |
dc.date.available | 2014-04-29T13:41:52Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | PROTEOMICS, 14 (4-5), p. 353-366 | - |
dc.identifier.issn | 1615-9853 | - |
dc.identifier.uri | http://hdl.handle.net/1942/16736 | - |
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. | - |
dc.description.sponsorship | SBO grant "InSPECtor" of the Flemish agency for Innovation by Science and Technology (IWT) (grant number 120025); Research Council of Norway; Ghent University (Multidisciplinary Research Partnership "Bioinformatics: from nucleotides to networks"); PRIME-XS project (grant number 262067); "ProteomeXchange" project (grant number 260558); European Union; ERC (grant number 240186) | - |
dc.language.iso | en | - |
dc.rights | © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim | - |
dc.subject.other | bioinformatics; machine learning; pattern recognition; shotgun proteomics; standardization | - |
dc.title | Machine learning applications in proteomics research: How the past can boost the future | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 366 | - |
dc.identifier.issue | 4-5 | - |
dc.identifier.spage | 353 | - |
dc.identifier.volume | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/pmic.201300289 | - |
dc.identifier.isi | 000332341200003 | - |
item.accessRights | Restricted Access | - |
item.contributor | Kelchtermans, Pieter | - |
item.contributor | Bittremieux, Wout | - |
item.contributor | De Grave, Kurt | - |
item.contributor | Degroeve, Sven | - |
item.contributor | Ramon, Jan | - |
item.contributor | Laukens, Kris | - |
item.contributor | VALKENBORG, Dirk | - |
item.contributor | Barsnes, Harald | - |
item.contributor | Martens, Lennart | - |
item.fullcitation | Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, Sven; Ramon, Jan; Laukens, Kris; VALKENBORG, Dirk; Barsnes, Harald & Martens, Lennart (2014) Machine learning applications in proteomics research: How the past can boost the future. In: PROTEOMICS, 14 (4-5), p. 353-366. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2015 | - |
crisitem.journal.issn | 1615-9853 | - |
crisitem.journal.eissn | 1615-9861 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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kelchtermans 1.pdf Restricted Access | Published version | 648.65 kB | Adobe PDF | View/Open Request a copy |
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