title: Predicting network activity from high throughput metabolomics.
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.
authors.affiliation: Emory Vaccine Center, Emory University, Atlanta, Georgia, USA. shuzhao.li@gmail.com
authors.forename: Shuzhao
authors.initials: S
authors.lastname: Li
authors.forename: Youngja
authors.initials: Y
authors.lastname: Park
authors.forename: Sai
authors.initials: S
authors.lastname: Duraisingham
authors.forename: Frederick H
authors.initials: FH
authors.lastname: Strobel
authors.forename: Nooruddin
authors.initials: N
authors.lastname: Khan
authors.forename: Quinlyn A
authors.initials: QA
authors.lastname: Soltow
authors.forename: Dean P
authors.initials: DP
authors.lastname: Jones
authors.forename: Bali
authors.initials: B
authors.lastname: Pulendran
mesh_terms: D000465:Algorithms; D006801:Humans; D007113:Immunity, Innate; D013058:Mass Spectrometry; D055432:Metabolomics