Sentence examples for mutual information thresholds from inspiring English sources
It takes a profile matrix M, a phenotype profile phe, the size of the set in a rule s and three mutual information thresholds, mMIp, mMIc and mMIr.
Fig. 2 Impact of the mutual information threshold.
First, selecting different mutual information threshold to determine whether two features are correlated impact the performance.
There are two important factors that would impact the performance of the AutoTL: the mutual information threshold ε of determining whether two features are correlated and the number of web pages selected as the source data.
Based on these studies, in the following experiments, we use 0.7 as the mutual information threshold and 10 web pages as the source data for the AutoTL by default.
In Network MDL the free parameter was set to 0.2 and in PMDL algorithm the conditional mutual information threshold was set to 0.1.
The searching for optimal solutions stops when the mutual information threshold backtracks to 0.7 and the correlation coefficient takes 0.3 and the P-value adopts 0.8 for the whole iterative procedure.
In this analysis, data processing inequality (DPI) tolerance was set to 0.20 and the mutual information (MI) threshold was 0.05.
Since biological theoreticians and experimentalists may vary specific mutual information and correlation thresholds to incorporate empirical or concrete knowledge into the reconstruction procedures, the underlying coordination approaches via the MOCO framework might be feasible and significant, especially for those containing pivotal structural connectivity or for specific analysis purposes.
Through the repeated permutation of clinical outcome labels and the re-computation of mutual information values, a threshold θ is defined as the maximum of the average mutual information values for each gene pair.
ARACNE adds edges between genes based on pairwise mutual information above a threshold, and prunes away the weakest edges in each triple of connected genes; they show that this removes many False Positive (FP) edges (the scope for false positive edges in sparse networks is large because there are so many possible networks).
