Essential cookies keep the library working. With your permission, we'd also like to use analytics to see what's useful and improve the experience. Read our Privacy Policy or Cookie Policy.
Checking your access to this record.
Weighted gene coexpression network analysis (WGCNA) has been applied to many important studies since its introduction in 2005. WGCNA can be used as a data exploratory tool or as a gene screening method; WGCNA can also be used as a tool to generate testable hypothesis for validation in independent data sets. In this article, we review key concepts of WGCNA and some of its applications in gene expression analysis of oncology, brain function, and protein interaction data.
Copy in another style
Zhao, W., Langfelder, P., Fuller, T. F., Dong, J., Li, A., & Hovarth, S. (2010). Weighted Gene Coexpression Network Analysis: State of the Art. Journal of Biopharmaceutical Statistics, 20(2), 281–300. https://doi.org/10.1080/10543400903572753
Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.
Esperanza Marcos, Belén Vela
In Revista Colombiana de Computación
Palash Dutta, Gourangajit Borah
In New Mathematics and Natural Computation
Xianfang Sun, Xin Mi Yang
In Computational Visual Media
P. A. M. Dirac
In Canadian Journal of Mathematics