Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/56378

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dc.contributor.authorCardoso, Sarapor
dc.contributor.authorBaptista, Delorapor
dc.contributor.authorSantos, R.por
dc.contributor.authorRocha, Miguelpor
dc.date.accessioned2018-10-18T17:13:36Z-
dc.date.issued2019-
dc.identifier.citationCardoso, Sara; Baptista, Delora; Santos, R.; Rocha, Miguel, A review on metabolomics data analysis for cancer applications. Advances in Intelligent Systems and Computing. Vol. 803 (PACBB 2018), Springer, 157-165, 2019.por
dc.identifier.isbn9783319987019por
dc.identifier.issn2194-5357por
dc.identifier.urihttps://hdl.handle.net/1822/56378-
dc.description.abstractCancer cells undergo metabolic changes that contribute to tumorigenesis, which can be determined using metabolomics data produced by techniques such as nuclear magnetic resonance and mass spectroscopy, and analyzed through statistical and machine learning methods. Since these data represent well the metabolic phenotype of these cells, they are very relevant in cancer research, to better understand tumour cells metabolism and help in efforts of biomarker and drug target discovery. This mini-review focuses on data analysis methods that are commonly used to extract knowledge from cancer metabolomics data, such as univariate analysis and supervised and unsupervised multivariate data analysis, including clustering and machine learning.por
dc.description.sponsorshipThis work is co-funded by the North Portugal Regional Operational Programme, under the “Portugal 2020”, through the European Regional Development Fund (ERDF), within project SISBI- RefaNORTE-01-0247-FEDER-003381. This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.por
dc.language.isoengpor
dc.publisherSpringerpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147337/PTpor
dc.rightsrestrictedAccesspor
dc.subjectCancerpor
dc.subjectMetabolomicspor
dc.subjectNMRpor
dc.subjectMass spectrometrypor
dc.subjectMachine learningpor
dc.subjectChemometricspor
dc.titleA review on metabolomics data analysis for cancer applicationspor
dc.typeconferencePaper-
dc.peerreviewedyespor
dc.relation.publisherversionhttp://www.springer.com/series/11156por
dc.commentsCEB48946por
oaire.citationStartPage157por
oaire.citationEndPage165por
oaire.citationVolume803por
dc.date.updated2018-10-18T10:04:14Z-
dc.identifier.eissn2194-5365por
dc.identifier.doi10.1007/978-3-319-98702-6_19por
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
dc.subject.wosScience & Technologypor
sdum.journalAdvances in Intelligent Systems and Computingpor
sdum.conferencePublicationPRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY AND BIOINFORMATICSpor
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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