Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/39892
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAGTEN, Annelies-
dc.contributor.authorCLAESEN, Jurgen-
dc.contributor.authorBURZYKOWSKI, Tomasz-
dc.contributor.authorVALKENBORG, Dirk-
dc.date.accessioned2023-03-30T10:38:03Z-
dc.date.available2023-03-30T10:38:03Z-
dc.date.issued2023-
dc.date.submitted2023-03-22T10:48:05Z-
dc.identifier.citationRAPID COMMUNICATIONS IN MASS SPECTROMETRY, 37 (9) (Art N° e9480)-
dc.identifier.urihttp://hdl.handle.net/1942/39892-
dc.description.abstractThe observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics. Sulphur atoms have a very distinctive elemental isotope definition and therefore, the presence of Sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Therefore, knowledge on the number of Sulphur atoms can improve identification of peptides and proteins.-
dc.description.sponsorshipFlanders AI research; Fonds Wetenschappelijk Onderzoek, Grant/Award Number: VS02819N The authors thank Geert Baggerman and Daniel Flender for providing the experimental data. The authors gratefully acknowledge funding from FWO-PAS grant VS02819N entitled “Computational methods for highresolution mass spectrometry data and massive parallel sequencing.” D.V. acknowledges funding from Flanders AI research.-
dc.language.isoen-
dc.publisher-
dc.rights2023John Wiley & Sons Ltd-
dc.titleMachine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution-
dc.typeJournal Contribution-
dc.identifier.issue9-
dc.identifier.volume37-
local.bibliographicCitation.jcatA1-
local.publisher.place111 RIVER ST, HOBOKEN 07030-5774, NJ USA-
local.type.refereedRefereed-
local.type.specifiedArticle-
local.bibliographicCitation.artnre9480-
dc.identifier.doi10.1002/rcm.9480-
dc.identifier.pmid36798055-
dc.identifier.isi000945773800001-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
local.provider.typeOrcid-
local.uhasselt.internationalyes-
item.accessRightsRestricted Access-
item.fullcitationAGTEN, Annelies; CLAESEN, Jurgen; BURZYKOWSKI, Tomasz & VALKENBORG, Dirk (2023) Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution. In: RAPID COMMUNICATIONS IN MASS SPECTROMETRY, 37 (9) (Art N° e9480).-
item.fulltextWith Fulltext-
item.contributorAGTEN, Annelies-
item.contributorCLAESEN, Jurgen-
item.contributorBURZYKOWSKI, Tomasz-
item.contributorVALKENBORG, Dirk-
crisitem.journal.issn0951-4198-
crisitem.journal.eissn1097-0231-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
rcm9480-sup-0001-appendix s1 (1).docx
  Restricted Access		Supplementary material439.88 kBMicrosoft WordView/Open    Request a copy
Rapid Comm Mass Spectrometry - 2023 - Agten - Machine learning approach for the prediction of the number of sulphur atoms.pdf
  Restricted Access		Peer-reviewed author version2.1 MBAdobe PDFView/Open    Request a copy
Rapid Comm Mass Spectrometry - 2023 - Agten - Machine learning approach for the prediction of the number of sulphur atoms.pdf
  Restricted Access		Published version2.1 MBAdobe PDFView/Open    Request a copy
Show simple item record

WEB OF SCIENCETM
Citations

1
checked on May 7, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.