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Harnessing the complexity of metabolomic data with chemometrics
Author(s) -
Boccard Julien,
Rudaz Serge
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2572
Subject(s) - chemometrics , metabolomics , relevance (law) , computer science , data science , curse of dimensionality , dimensionality reduction , data mining , biochemical engineering , machine learning , bioinformatics , biology , engineering , political science , law
Metabolomics constitutes a representative example of fast moving research fields taking advantage of recent technological advances to provide extensive sample monitoring. Challenges related to making use of the wealth of data generated include extracting relevant elements within massive amounts of signals possibly spread across different tables, reducing dimensionality, summarising dynamic information in a comprehensible way and displaying it for interpretation purposes. The added‐value of most metabolomic models is not restricted to their statistical significance but also strongly related to their biological relevance. Understandable metabolic information unravelling the role of each variable is therefore needed. Dedicated modelling algorithms, able to cope with the inherent properties of these metabolomic datasets are mandatory for harnessing their complexity and provide relevant information. In that perspective, chemometrics has a central role to play.