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Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics.

Mol. Nutr. Food Res. 53, 1357-1365 (2009)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Identifying the biochemical basis of microbial phenotypes is a main objective of comparative genomics. Here we present a novel method using multivariate machine learning techniques for comparing automatically derived metabolic reconstructions of sequenced genomes on a large scale. Applying our method to 266 genomes directly led to testable hypotheses such as the link between the potential of microorganisms to cause periodontal disease and their ability to degrade histidine, a link also supported by clinical studies.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Acylcarnintine; Coffee; Metabolomics; MS/MS; Sphingomyelin
ISSN (print) / ISBN 1613-4125
e-ISSN 1613-4133
Quellenangaben Band: 53, Heft: 11, Seiten: 1357-1365 Artikelnummer: , Supplement: ,
Verlag Wiley
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Epidemiology (EPI)