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Weighted correlation networks facilitate a geometric interpretation based on the angular interpretation of the correlation, chapter 6 in. [4] Resulting network statistics can be used to enhance standard data-mining methods such as cluster analysis since (dis)-similarity measures can often be transformed into weighted networks; [ 5 ] see chapter ...
For example, weighted gene co-expression network analysis (WGCNA) is often used for constructing a weighted network among genes (or gene products) based on gene expression (e.g. microarray) data. [9] More generally, weighted correlation networks can be defined by soft-thresholding the pairwise correlations among variables (e.g. gene ...
The concept of gene co-expression networks was first introduced by Butte and Kohane in 1999 as relevance networks. [6] They gathered the measurement data of medical laboratory tests (e.g. hemoglobin level ) for a number of patients and they calculated the Pearson correlation between the results for each pair of tests and the pairs of tests which showed a correlation higher than a certain level ...
As an example, weighted gene co-expression network analysis uses Pearson correlation to analyze linked gene expression and understand genetics at a systems level. [49] Another measure of correlation is linkage disequilibrium. Linkage disequilibrium describes the non-random association of genetic sequences among loci in a given chromosome. [50]
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.
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Biweight midcorrelation has been shown to be more robust in evaluating similarity in gene expression networks, [2] and is often used for weighted correlation network analysis. Implementations [ edit ]