Eigengene networks for studying the relationships between co-expression modules
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1752-0509
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Eigengene networks for studying the relationships between co-expression modules ; volume:1 ; number:1 ; day:21 ; month:11 ; year:2007 ; pages:1-17 ; date:12.2007
BMC systems biology ; 1, Heft 1 (21.11.2007), 1-17, 12.2007
- Urheber
-
Langfelder, Peter
- Beteiligte Personen und Organisationen
-
Horvath, Steve
SpringerLink (Online service)
- DOI
-
10.1186/1752-0509-1-54
- URN
-
urn:nbn:de:1111-2016072947183
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:52 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Langfelder, Peter
- Horvath, Steve
- SpringerLink (Online service)
Ähnliche Objekte (12)
Understanding network concepts in modules
DNA methylation age of human tissues and cell types
Comparison of co-expression measures: mutual information, correlation, and model based indices
Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease
Gene network interconnectedness and the generalized topological overlap measure
WGCNA: an R package for weighted correlation network analysis
Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells
Weighted gene coexpression network analysis strategies applied to mouse weight
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks
Random generalized linear model: a highly accurate and interpretable ensemble predictor
Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome
Multivariate variance-components analysis of longitudinal blood pressure measurements from the Framingham Heart Study
Understanding network concepts in modules
DNA methylation age of human tissues and cell types
Comparison of co-expression measures: mutual information, correlation, and model based indices
Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease
Gene network interconnectedness and the generalized topological overlap measure
WGCNA: an R package for weighted correlation network analysis
Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells
Weighted gene coexpression network analysis strategies applied to mouse weight
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks
Random generalized linear model: a highly accurate and interpretable ensemble predictor
Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome
Multivariate variance-components analysis of longitudinal blood pressure measurements from the Framingham Heart Study
Understanding network concepts in modules
DNA methylation age of human tissues and cell types
Comparison of co-expression measures: mutual information, correlation, and model based indices
Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease
Gene network interconnectedness and the generalized topological overlap measure
WGCNA: an R package for weighted correlation network analysis
Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells
Weighted gene coexpression network analysis strategies applied to mouse weight
Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks
Random generalized linear model: a highly accurate and interpretable ensemble predictor
Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome