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2000
Volume 12, Issue 7
  • ISSN: 1389-2010
  • E-ISSN: 1873-4316

Abstract

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.

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/content/journals/cpb/10.2174/138920111795909041
2011-07-01
2025-06-27
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/content/journals/cpb/10.2174/138920111795909041
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