[HTML][HTML] Predicting network activity from high throughput metabolomics
PLoS computational biology, 2013•journals.plos.org
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.
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.
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.
PLOS