Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data (Englisch)
Freier Zugriff
- Neue Suche nach: Liu, Zhi-Ping
- Neue Suche nach: Zhang, Wanwei
- Neue Suche nach: Horimoto, Katsuhisa
- Neue Suche nach: Chen, Luonan
- Neue Suche nach: Liu, Zhi-Ping
- Neue Suche nach: Zhang, Wanwei
- Neue Suche nach: Horimoto, Katsuhisa
- Neue Suche nach: Chen, Luonan
In:
IET Systems Biology
;
7
, 5
;
143-152
;
2013
- Aufsatz (Zeitschrift) / Elektronische Ressource
-
Titel:Gaussian graphical model for identifying significantly responsive regulatory networks from time course high-throughput data
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Beteiligte:Liu, Zhi-Ping ( Autor:in ) / Zhang, Wanwei ( Autor:in ) / Horimoto, Katsuhisa ( Autor:in ) / Chen, Luonan ( Autor:in )
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Erschienen in:IET Systems Biology ; 7, 5 ; 143-152
-
Verlag:
- Neue Suche nach: The Institution of Engineering and Technology
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Erscheinungsdatum:01.10.2013
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Format / Umfang:10 pages
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ISSN:
-
DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:circadian rhythm , genomics , phenotypes , simulated time course microarray data , functional linkages , responsive regulatory networks , consistency measurement , knowledge-based networks , genetics , dynamic Bayesian network model , matching network structure , molecular biophysics , databases , proteins , Gaussian processes , speciflc gene expression proflling data , documented regulatory networks , true time course microarray data , circadian rhythms , Bayes methods , dynamic gene expression proflling , time course high-throughput data , biology computing , network architectures , biological molecules , Gaussian graphical model , time course gene expression proflles , graphs
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Datenquelle:
Metadata by IET is licensed under CC BY 3.0
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