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Predicting network activity from high throughput metabolomics.
- Source :
- PLoS Computational Biology, Vol 9, Iss 7, p e1003123 (2013)
- Publication Year :
- 2013
- Publisher :
- Public Library of Science (PLoS), 2013.
-
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.
- Subjects :
- Biology (General)
QH301-705.5
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Volume :
- 9
- Issue :
- 7
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.57501b2771d4ce494384c53719163f1
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1003123