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WGCNA can be used as a data reduction technique (related to oblique factor analysis), as a clustering method (fuzzy clustering), as a feature selection method (e.g. as gene screening method), as a framework for integrating complementary (genomic) data (based on weighted correlations between quantitative variables), and as a data exploratory ...
WGCNA is an R package for weighted correlation network analysis. Pigengene is an R package that infers biological information from gene expression profiles. Based on a coexpression network, it computes eigengenes and effectively uses them as features to fit decision trees and Bayesian networks that are useful in diagnosis and prognosis.
Biological network inference is the process of making inferences and predictions about biological networks. [1] By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these interactions between species, DNA, proteins, and more.
Colorado head coach Deion Sanders issued a warning to NFL teams Friday − don’t draft Heisman Trophy winner Travis Hunter if you won’t let him play both ways.. Sanders said this on "The Rich ...
The U.S. House of Representatives is expected to consider on Thursday what to do with a report on alleged sexual misconduct and drug use by ex-congressman Matt Gaetz, who has dropped his bid to ...
Biweight midcorrelation has been implemented in the R statistical programming language as the function bicor as part of the WGCNA package [3] Also implemented in the Raku programming language as the function bi_cor_coef as part of the Statistics module.
A total of 5.94 million deaths were prevented for those five disease types, according to an NCI press release. Eighty percent of those averted deaths were attributed to screening and prevention.
Moreover, the WGCNA method constructs a weighted network which means all possible edges appear in the network, but each edge has a weight which shows how significant is the co-expression relationship corresponding to that edge. Of note, threshold selection is intended to coerce networks into a scale-free topology.