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By classifying outcomes as a categorical variable, clusters of 'potentially low' outcomes can also be mapped, thereby identifying populations whose recovery status may decrease.
The GA methodology provides a large collection of variable clusters.
Most of the differentially expressed probes belonged to the two most variable clusters.
64 We also considered subsetting variable clusters based on the frequency of their selection by GA.
The procedure starts by generating a random population of variable clusters.
Detection of such variable clusters was a characteristic of our method.
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First, a cluster analysis was performed, using model-based (Mclust) and variable clustering.
We explored the relationships among these phenotypes using variable clustering and then estimated their genetic heritabilities and cross-trait correlations.
Exploratory analyses noted that medication class had a larger impact on these relationships than the number of psychiatric medications in use.In a BDI sample, variable cluster analysis was able to group related chronobiological variables.
The aim of this study was to conduct a variable cluster analysis in order to ascertain how mood states are associated with chronobiological traits in bipolar I disorder (BDI).
Specifically, an iterative approach is developed by integrating the technical strengths of Variable Clustering and Random Forest to remove collinearity, redundancy and nonrelevance.
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