Predicting Network Activity from High Throughput Metabolomics
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Shuzhao | - |
dc.contributor.author | Park, Youngja | - |
dc.contributor.author | Duraisingham, Sai | - |
dc.contributor.author | Strobel, Frederick H. | - |
dc.contributor.author | Khan, Nooruddin | - |
dc.contributor.author | Soltow, Quinlyn A. | - |
dc.contributor.author | Jones, Dean P. | - |
dc.contributor.author | Pulendran, Bali | - |
dc.date.accessioned | 2021-09-06T00:01:47Z | - |
dc.date.available | 2021-09-06T00:01:47Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-07 | - |
dc.identifier.issn | 1553-734X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/102766 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | PUBLIC LIBRARY SCIENCE | - |
dc.subject | YELLOW-FEVER VACCINE | - |
dc.subject | METABOLITE IDENTIFICATION | - |
dc.subject | MURINE CYTOMEGALOVIRUS | - |
dc.subject | FUNCTIONAL MODULES | - |
dc.subject | RECONSTRUCTION | - |
dc.subject | INNATE | - |
dc.subject | CELLS | - |
dc.subject | EXPRESSION | - |
dc.subject | MODULARITY | - |
dc.subject | DATABASE | - |
dc.title | Predicting Network Activity from High Throughput Metabolomics | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Youngja | - |
dc.identifier.doi | 10.1371/journal.pcbi.1003123 | - |
dc.identifier.scopusid | 2-s2.0-84880851838 | - |
dc.identifier.wosid | 000322320200011 | - |
dc.identifier.bibliographicCitation | PLOS COMPUTATIONAL BIOLOGY, v.9, no.7 | - |
dc.relation.isPartOf | PLOS COMPUTATIONAL BIOLOGY | - |
dc.citation.title | PLOS COMPUTATIONAL BIOLOGY | - |
dc.citation.volume | 9 | - |
dc.citation.number | 7 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | YELLOW-FEVER VACCINE | - |
dc.subject.keywordPlus | METABOLITE IDENTIFICATION | - |
dc.subject.keywordPlus | MURINE CYTOMEGALOVIRUS | - |
dc.subject.keywordPlus | FUNCTIONAL MODULES | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | INNATE | - |
dc.subject.keywordPlus | CELLS | - |
dc.subject.keywordPlus | EXPRESSION | - |
dc.subject.keywordPlus | MODULARITY | - |
dc.subject.keywordPlus | DATABASE | - |
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