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Weighted correlation network analysis, also known as weighted gene co-expression network analysis (WGCNA), is a widely used data mining method especially for studying biological networks based on pairwise correlations between variables.
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]
Topology Analysis analyzes the topology of a network to identify relevant participates and substructures that may be of biological significance. The term encompasses an entire class of techniques such as network motif search, centrality analysis, topological clustering, and shortest paths. These are but a few examples, each of these techniques ...
President-elect Donald Trump is still putting together his Cabinet for his second term, and many other government positions will open up when he takes the oath of office in January.
Unlike their 2020 research that found no significant relationship between overall PM2.5 and childhood cognition, this new analysis focused on 15 specific chemical components within PM2.5.
For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets; pathway-based methods for omics data ...