Search results
Results from the WOW.Com Content Network
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]
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.
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 ]
The Trump transition team wants the incoming administration to drop a car-crash reporting requirement opposed by Elon Musk’s Tesla, according to a document seen by Reuters, a move that could ...
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. [140]