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PLoS Comput Biol. 2013;9(7):e1003123. doi: 10.1371/journal.pcbi.1003123. Epub 2013 Jul 4.

Predicting network activity from high throughput metabolomics.

Author information

1
Emory Vaccine Center, Emory University, Atlanta, Georgia, USA. shuzhao.li@gmail.com

Abstract

The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

PMID:
23861661
PMCID:
PMC3701697
DOI:
10.1371/journal.pcbi.1003123
[Indexed for MEDLINE]
Free PMC Article

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