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From data processing to multivariate validation: essential steps in extracting interpretable information from metabolomics data
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
2011 (engelsk)Inngår i: Current Pharmaceutical Biotechnology, ISSN 1389-2010, E-ISSN 1873-4316, Vol. 12, nr 7, s. 996-1004Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

In metabolomics studies there is a clear increase of data. This indicates the necessity of both having a battery of suitable analysis methods and validation procedures able to handle large amounts of data. In this review, an overview of the metabolomics data processing pipeline is presented. A selection of recently developed and most cited data processing methods is discussed. In addition, commonly used chemometric and machine learning analysis methods as well as validation approaches are described.

sted, utgiver, år, opplag, sider
Bentham Science Publishers , 2011. Vol. 12, nr 7, s. 996-1004
Emneord [en]
multivariate data analysis, data processing, chemometrics, metabolomics, statistical validation, validation procedures, chemometric and machine learning analysis, NMR, downstream data analysis, Filtration, non-linear regression method
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Identifikatorer
URN: urn:nbn:se:umu:diva-44567DOI: 10.2174/138920111795909041ISI: 000292758600004PubMedID: 21466461OAI: oai:DiVA.org:umu-44567DiVA, id: diva2:421438
Tilgjengelig fra: 2011-06-08 Laget: 2011-06-08 Sist oppdatert: 2018-06-08bibliografisk kontrollert

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