WGCNA: an R package for weighted correlation network analysis
about
When is hub gene selection better than standard meta-analysis?Epigenetic predictor of ageComparison of co-expression measures: mutual information, correlation, and model based indicesIntegration of miRNA and protein profiling reveals coordinated neuroadaptations in the alcohol-dependent mouse brainA modular organization of the human intestinal mucosal microbiota and its association with inflammatory bowel diseaseTemporal specification and bilaterality of human neocortical topographic gene expressionCHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitorsRare coding variants in the phospholipase D3 gene confer risk for Alzheimer's diseaseA novel IFITM5 mutation in severe atypical osteogenesis imperfecta type VI impairs osteoblast production of pigment epithelium-derived factorNext generation sequencing technology and genomewide data analysis: Perspectives for retinal researchMulti-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfareTranscriptome Analysis in Domesticated Species: Challenges and StrategiesMutations and Modeling of the Chromatin Remodeler CHD8 Define an Emerging Autism EtiologyGenomic, Proteomic, and Metabolomic Data Integration StrategiesTIN: An R Package for Transcriptome Instability AnalysisBeyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disordersA progressive mouse model of Parkinson's disease: the Thy1-aSyn ("Line 61") miceGenetic, transcriptomic, and epigenetic studies of HIV-associated neurocognitive disorderPathway mapping and development of disease-specific biomarkers: protein-based network biomarkersGene expression profiling of the brain: pondering facts and fictionMicroglia recapitulate a hematopoietic master regulator network in the aging human frontal cortexAdvanced Applications of RNA Sequencing and ChallengesLearning from Co-expression Networks: Possibilities and ChallengesProteomic approaches and identification of novel therapeutic targets for alcoholismH2-saturation of high affinity H2-oxidizing bacteria alters the ecological niche of soil microorganisms unevenly among taxonomic groups.Efficient and biologically relevant consensus strategy for Parkinson's disease gene prioritization.Integrated Systems Biology Analysis of Transcriptomes Reveals Candidate Genes for Acidity Control in Developing Fruits of Sweet Orange (Citrus sinensis L. Osbeck).Genetic Architecture of Atherosclerosis in Mice: A Systems Genetics Analysis of Common Inbred StrainsIdentification of Chemical Inhibitors of β-Catenin-Driven Liver Tumorigenesis in ZebrafishAdult mouse cortical cell taxonomy revealed by single cell transcriptomicsTau overexpression impacts a neuroinflammation gene expression network perturbed in Alzheimer's diseaseLiver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle.Intrahepatic Transcriptional Signature Associated with Response to Interferon-α Treatment in the Woodchuck Model of Chronic Hepatitis BTranscriptional and Hormonal Regulation of Gravitropism of Woody Stems in PopulusImmune-Mediated Inflammation May Contribute to the Pathogenesis of Cardiovascular Disease in Mucopolysaccharidosis Type IAssessment of fight outcome is needed to activate socially driven transcriptional changes in the zebrafish brain.An Ultrasensitive Mechanism Regulates Influenza Virus-Induced InflammationFoxP1 orchestration of ASD-relevant signaling pathways in the striatum.Modeling host genetic regulation of influenza pathogenesis in the collaborative crossAnalysis of the dynamic co-expression network of heart regeneration in the zebrafish.
P2860
Q21133557-1191D517-E59D-4FB6-938C-2E5DB568C71FQ21135613-DE8C1AF1-E823-4001-977E-E75180FAB795Q21284318-59FC6E69-FFAB-47E4-A4D5-A6342F66AAC2Q21559493-667E3FEA-0610-453F-8E3C-2EDA43DB69A3Q21559533-AF745A20-F7BD-4925-9051-F71C1A36D773Q22337323-8E6F067E-8A52-41B8-A8FC-46A84CC9E436Q24305288-8BCC82A8-ABB7-4A66-864B-5DBB12CD16D5Q24312938-E4EAE5A6-ED9A-49F6-A053-A472D355D224Q24336923-A75C42FF-F45B-404C-AB5B-CA00771FABE4Q26746206-2FB64DB4-8796-43E0-B047-8BFFA87BF809Q26748831-5A0F89E9-37A6-4874-8990-1C9FE11BF651Q26766287-2CF6AD66-25FB-408E-9D84-004FF30227CCQ26770773-A82C5E38-F892-4C2F-8F44-81B3922F05C7Q26781985-5E2A06DD-28A1-45FC-8367-5B5D95A0977AQ26786118-788CFD70-A86D-4156-A8A5-8AE639FD5369Q26830010-D48FC76C-2DDF-4937-893E-84E6E0765C53Q26859072-2E613D83-6F62-4CCE-9699-DF20A5AC20F9Q26865707-BE43A9A9-1853-4D67-9294-C1CF02D62217Q26995884-6D443913-3D49-497E-9CF2-68D21DF1A81CQ27010353-D853723E-053B-4C61-9CB4-F8499F2B1C13Q27011558-2F603534-A9C6-4DED-B24A-91EED3C01CF7Q27013028-41FEA467-9E6D-4222-988A-FC92C6EFB23DQ27015436-6E8A2712-890E-419F-8B5C-DDF223BB0D9EQ27023085-E19E7F14-6448-4148-953B-9916D83A8B30Q27300184-319970FE-FA15-4745-AF4A-D6A495F41D2EQ27304699-C0E16AB4-2885-4C8C-B47D-4ADB6CFAF722Q27307083-4345FBBC-5FD1-4BFB-8583-54A1A8ED9BFDQ27309881-B7F41546-FCE2-4116-985B-CC4E3611EFE6Q27310913-20AF78F7-D45C-42B2-9451-70569768B802Q27311570-76B31BC3-31AF-4757-8981-C555E1D14778Q27313627-D208C5AF-B1FC-4020-BC20-1612DDD8FA78Q27316949-C28130F8-BE91-4C44-9F15-16DF2D8C1364Q27318089-3B524377-D30C-4811-93A7-C69AB2867BFEQ27318523-FAD8FF2F-8B6E-4BBF-AB5B-6EBA56BACBF1Q27318642-DCBADA27-0842-4452-8F70-ED82B778A9D2Q27319460-7F5D1317-CAF1-4737-968F-7215ABBD343BQ27320092-DA23842A-A9C7-4010-940D-B312239CEBCDQ27322723-569AE822-3F8A-4B5C-8537-4F0307EAC30EQ27336134-3A2734FA-6EDC-410C-AFD7-BC463F77FDE8Q27349288-33349DD8-C6B6-4CC0-8C09-82B44E02C131
P2860
WGCNA: an R package for weighted correlation network analysis
description
2008 nî lūn-bûn
@nan
2008 թուականին հրատարակուած գիտական յօդուած
@hyw
2008 թվականին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
WGCNA: an R package for weighted correlation network analysis
@ast
WGCNA: an R package for weighted correlation network analysis
@en
WGCNA: an R package for weighted correlation network analysis
@en-gb
WGCNA: an R package for weighted correlation network analysis
@nl
type
label
WGCNA: an R package for weighted correlation network analysis
@ast
WGCNA: an R package for weighted correlation network analysis
@en
WGCNA: an R package for weighted correlation network analysis
@en-gb
WGCNA: an R package for weighted correlation network analysis
@nl
prefLabel
WGCNA: an R package for weighted correlation network analysis
@ast
WGCNA: an R package for weighted correlation network analysis
@en
WGCNA: an R package for weighted correlation network analysis
@en-gb
WGCNA: an R package for weighted correlation network analysis
@nl
P2860
P3181
P356
P1433
P1476
WGCNA: an R package for weighted correlation network analysis
@en
P2093
Peter Langfelder
Steve Horvath
P2860
P2888
P3181
P356
10.1186/1471-2105-9-559
P407
P577
2008-01-01T00:00:00Z
P5875
P6179
1020312314