Predicting network activity from high throughput metabolomics.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B. (2013). Predicting network activity from high throughput metabolomics. PLoS computational biology. PMID 23861661 · PMC3701697 · DOI 10.1371/journal.pcbi.1003123

证据类型：BENCHMARK

主题：metabolomics

被引次数（采集时）：792

原始记录：[Europe PMC](https://europepmc.org/article/MED/23861661)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

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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