Results 151 to 160 of about 69,571 (187)

Weighted gene co-expression network analysis - based selection of hub genes related to phenolic and volatile compounds and seed coat color in sorghum. [PDF]

BMC Plant Biol
Lee YJ, Kim WJ, Lee SH, Kim JH, Kwon SJ, Ahn JW, Kim SH, Kim JB, Lyu JI, Bae CH, Ryu J.   +10 more
europepmc   +1 more source

Molecular signatures of preeclampsia subtypes determined through integrated weighted gene co-expression network analysis and differential gene expression analysis of placental transcriptomics. [PDF]

Front Cell Dev Biol
Han L, da Silva Costa F, Perkins A, Holland O.   +3 more
europepmc   +1 more source

Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning. [PDF]

Int J Mol Sci
Sun Q, Zhang HL, Wang Y, Xiu H, Lu Y, He N, Yin L.   +6 more
europepmc   +1 more source

Weighted Gene Co-Expression Network Analysis Uncovers Core Drought Responsive Genes in Pecan (<i>Carya illinoinensis</i>). [PDF]

Plants (Basel)
Hou M, Li Y, Xuan J, Zhang Y, Wang T, Zhai M, Wang G, Hu L, Mo Z.   +8 more
europepmc   +1 more source

Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer. [PDF]

Sci Rep
Mantini G, Agostini A, Tufo M, Rossi S, Kulesko M, Carbone C, Salvatore L, Tortora G, Scambia G, Giacò L.   +9 more
europepmc   +1 more source

Identification of lncRNAs in peripheral blood mononuclear cells associated with sepsis immunosuppression based on weighted gene co-expression network analysis. [PDF]

Hereditas
Zhang W, Li Y, Li G, Zhang A, Sun W.
europepmc   +1 more source

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A Genetic Algorithm to Optimize Weighted Gene Co-Expression Network Analysis

Journal of Computational Biology, 2019
Weighted gene co-expression network analysis (WGCNA) is a widely used software tool that is used to establish relationships between phenotypic traits and gene expression data. It generates gene modules and then correlates their first principal component to phenotypic traits, proposing a functional relationship expressed by the correlation coefficient ...
David Toubiana, Rami Puzis, Avi Sadka
exaly   +3 more sources

Identifying key genes in rheumatoid arthritis by weighted gene co‐expression network analysis

International Journal of Rheumatic Diseases, 2017
AbstractPurposeThis study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co‐expression network analysis) method.MethodsTwo gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene ...
Qi Lv, Yinxian Yu, Kerun Niu
exaly   +3 more sources

Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co‐expression network analysis

Journal of Cellular Biochemistry, 2019
AbstractHepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical
Ke Pu, Xinan Wu
exaly   +3 more sources

Weighted Gene Co-Expression Network Analysis Reveals Dysregulation of Mitochondrial Oxidative Phosphorylation in Eating Disorders

Genes, 2018
The underlying mechanisms of eating disorders (EDs) are very complicated and still poorly understood. The pathogenesis of EDs may involve the interplay of multiple genes.
Liulin Yang, Turki Turki, Huizi Tan
exaly   +2 more sources