Predicting Network Activity from High Throughput Metabolomics |
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| Authors: | Shuzhao Li Youngja Park Sai Duraisingham Frederick H. Strobel Nooruddin Khan Quinlyn A. Soltow Dean P. Jones Bali Pulendran |
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| Affiliation: | 1.Emory Vaccine Center, Emory University, Atlanta, Georgia, United States of America;2.Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, United States of America;3.Department of Medicine, Emory University, Atlanta, Georgia, United States of America;4.College of Pharmacy, Korea University, Seoul, South Korea;5.Mass Spectrometry Center, Emory University, Atlanta, Georgia, United States of America;The Centre for Research and Technology, Hellas, Greece |
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| 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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