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TitleA review on metabolomics data analysis for cancer applications
Author(s)Cardoso, Sara
Baptista, Delora
Santos, R.
Rocha, Miguel
Mass spectrometry
Machine learning
Issue date2019
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
Publisher version
AccessRestricted access (UMinho)
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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