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Predicting Network Activity from High Throughput Metabolomics
by
Duraisingham, Sai
, Strobel, Frederick H.
, Soltow, Quinlyn A.
, Khan, Nooruddin
, Jones, Dean P.
, Pulendran, Bali
, Park, Youngja
, Li, Shuzhao
in
Algorithms
/ Biology
/ Computational biology
/ Experiments
/ Gene expression
/ Genomes
/ Humans
/ Immunity, Innate
/ Mass Spectrometry
/ Metabolites
/ Metabolomics
/ Physiological aspects
2013
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Predicting Network Activity from High Throughput Metabolomics
by
Duraisingham, Sai
, Strobel, Frederick H.
, Soltow, Quinlyn A.
, Khan, Nooruddin
, Jones, Dean P.
, Pulendran, Bali
, Park, Youngja
, Li, Shuzhao
in
Algorithms
/ Biology
/ Computational biology
/ Experiments
/ Gene expression
/ Genomes
/ Humans
/ Immunity, Innate
/ Mass Spectrometry
/ Metabolites
/ Metabolomics
/ Physiological aspects
2013
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Do you wish to request the book?
Predicting Network Activity from High Throughput Metabolomics
by
Duraisingham, Sai
, Strobel, Frederick H.
, Soltow, Quinlyn A.
, Khan, Nooruddin
, Jones, Dean P.
, Pulendran, Bali
, Park, Youngja
, Li, Shuzhao
in
Algorithms
/ Biology
/ Computational biology
/ Experiments
/ Gene expression
/ Genomes
/ Humans
/ Immunity, Innate
/ Mass Spectrometry
/ Metabolites
/ Metabolomics
/ Physiological aspects
2013
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Predicting Network Activity from High Throughput Metabolomics
Journal Article
Predicting Network Activity from High Throughput Metabolomics
2013
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Overview
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.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
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