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Predicting Network Activity from High Throughput Metabolomics

Authors
Li, ShuzhaoPark, YoungjaDuraisingham, SaiStrobel, Frederick H.Khan, NooruddinSoltow, Quinlyn A.Jones, Dean P.Pulendran, Bali
Issue Date
7월-2013
Publisher
PUBLIC LIBRARY SCIENCE
Citation
PLOS COMPUTATIONAL BIOLOGY, v.9, no.7
Indexed
SCIE
SCOPUS
Journal Title
PLOS COMPUTATIONAL BIOLOGY
Volume
9
Number
7
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/102766
DOI
10.1371/journal.pcbi.1003123
ISSN
1553-734X
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.
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