Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 9, Issue 7, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1003123
Keywords
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Funding
- US National Institutes of Health [AG038746, ES016731, U19AI090023, U54AI057157, R37AI48638, R37DK057665, U19AI057266, PO1A1096187]
- Scripps CHAVI-ID Award [UM1AI100663]
- Bill and Melinda Gates Foundation
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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.
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