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Weighted correlation network analysis can be attractive for the following reasons: The network construction (based on soft thresholding the correlation coefficient) preserves the continuous nature of the underlying correlation information. For example, weighted correlation networks that are constructed on the basis of correlations between ...
Weighted networks are also widely used in genomic and systems biologic applications. [3] 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 ...
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. [50] Another measure of correlation is linkage disequilibrium. Linkage disequilibrium describes the non-random association of genetic sequences among loci in a given chromosome. [51]
Steve Horvath is a German–American aging researcher, geneticist, and biostatistician.He is a professor at the University of California, Los Angeles known for developing the Horvath aging clock, which is a highly accurate molecular biomarker of aging, and for developing weighted correlation network analysis.
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 ]
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.
GeneNetwork is a combined database and open-source bioinformatics data analysis software resource for systems genetics. [1] This resource is used to study gene regulatory networks that link DNA sequence differences to corresponding differences in gene and protein expression and to variation in traits such as health and disease risk.