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The details of the module detection method are described in Materials and Methods.
A simulation study was performed to assess the performance of the module detection method.
Our module detection method takes the eQTL mapping results as the input and can be used with any eQTL mapping method; therefore it provides the flexibility to reanalyze the eQTL network when new algorithms for eQTL mapping become available.
Our module detection method performed reasonably well with a NMI value above 0.8 when the module homogeneity was higher than 0.6, and the NMI value was very close to its maximum value of 1.0 when the module homogeneity is higher than 0.9.
A step-by-step network construction and module detection method was used in our study.
Using simulated data to assess the performance of the consensus module detection method.
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Our theoretical results will apply to all module detection methods that result in approximately factorizable networks.
Many module detection methods identify groups of genes whose expression profiles are highly correlated.
This insight could be interesting to researchers who develop module detection methods.
The differences can be countered by using appropriate data set specific module detection methods.
Thus, in the following comparisons with other module detection methods, this prime parameter setting has been used.
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module discovery method
module detection methods
module detection analysis
module detection algorithm
module map method
module detection strategy
module identification method
module reuse method
module comparison method
module detection problem
module inference method
module search method
module detection approach
module selection method
module prediction method
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