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

TitleA review on metabolomics data analysis for cancer applications
Author(s)Cardoso, Sara
Baptista, Delora
Santos, R.
Rocha, Miguel
KeywordsCancer
Metabolomics
NMR
Mass spectrometry
Machine learning
Chemometrics
Issue date2019
PublisherSpringer
JournalAdvances in Intelligent Systems and Computing
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.
Abstract(s)Cancer 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.
TypeConference paper
URIhttps://hdl.handle.net/1822/56378
ISBN9783319987019
DOI10.1007/978-3-319-98702-6_19
ISSN2194-5357
e-ISSN2194-5365
Publisher versionhttp://www.springer.com/series/11156
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

Files in This Item:
File Description SizeFormat 
document_48946_1.pdf
  Restricted access
209,08 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID