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Numerical Representations of Metabolic Systems

[Image: see text] Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic. Yet, a systematic treatment of the fundamental structure and properties of metabolomics data is lagg...

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Autores principales: Smilde, Age K., Hankemeier, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2020
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581014/
https://www.ncbi.nlm.nih.gov/pubmed/32991165
http://dx.doi.org/10.1021/acs.analchem.9b05613
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author Smilde, Age K.
Hankemeier, Thomas
author_facet Smilde, Age K.
Hankemeier, Thomas
author_sort Smilde, Age K.
collection PubMed
description [Image: see text] Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic. Yet, a systematic treatment of the fundamental structure and properties of metabolomics data is lagging behind. We want to fill this gap by introducing two fundamental theories concerning metabolomics data: data theory and measurement theory. Our approach is to ask simple questions, the answers of which require applying these theories to metabolomics. We show that we can distinguish at least four different levels of metabolomics data with different properties and warn against confusing data with numbers. This treatment provides a theoretical underpinning for preprocessing and postprocessing methods in metabolomics and also argues for a proper match between type of metabolomics data and the biological question to be answered. The approach can be extended to other omics measurements such as proteomics and is thus of relevance for a large analytical chemistry community.
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spelling pubmed-75810142020-10-26 Numerical Representations of Metabolic Systems Smilde, Age K. Hankemeier, Thomas Anal Chem [Image: see text] Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic. Yet, a systematic treatment of the fundamental structure and properties of metabolomics data is lagging behind. We want to fill this gap by introducing two fundamental theories concerning metabolomics data: data theory and measurement theory. Our approach is to ask simple questions, the answers of which require applying these theories to metabolomics. We show that we can distinguish at least four different levels of metabolomics data with different properties and warn against confusing data with numbers. This treatment provides a theoretical underpinning for preprocessing and postprocessing methods in metabolomics and also argues for a proper match between type of metabolomics data and the biological question to be answered. The approach can be extended to other omics measurements such as proteomics and is thus of relevance for a large analytical chemistry community. American Chemical Society 2020-09-29 2020-10-20 /pmc/articles/PMC7581014/ /pubmed/32991165 http://dx.doi.org/10.1021/acs.analchem.9b05613 Text en © 2020 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
spellingShingle Smilde, Age K.
Hankemeier, Thomas
Numerical Representations of Metabolic Systems
title Numerical Representations of Metabolic Systems
title_full Numerical Representations of Metabolic Systems
title_fullStr Numerical Representations of Metabolic Systems
title_full_unstemmed Numerical Representations of Metabolic Systems
title_short Numerical Representations of Metabolic Systems
title_sort numerical representations of metabolic systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581014/
https://www.ncbi.nlm.nih.gov/pubmed/32991165
http://dx.doi.org/10.1021/acs.analchem.9b05613
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