A fitness function for the GABi biclustering framework, based on the simple principle that the larger the dense submatrix, the more interesting it is to discover.
Numeric vector representing a GA solution to the biclustering problem (i.e. a subset of the columns from
A fitness function is fundamental to the success of a GA. In this case,
getFitnesses.basic evaluates the desirability of biclusters by multiplying the number of columns from dataset
x (argument for function
GABi) that displaying a consistent block of
1s involving the features that are observed to fit this pattern. Makes use of
fitnessArgs a list of parameters in the environment of execution of the biclustering function
GABi. Notably, the element
consistency is used to apply a stringency threshold for selecting features (i.e. only those with the proportion of high values across the subset of samples being greater than
Numeric value representing fitness score for the solution encoded by
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