A fitness function for the GABi biclustering framework, based on the principle that the less likely a bicluster would be observed by chance, the more interesting it is to discover that bicluster.
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.entropy evaluates the desirability of biclusters by estimating the probability of a given selection of the columns from dataset
x (argument for function
GABi) 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
featureWeights is a numeric vector encoding the probability of any randomly selected column of the input matrix
x having a high value of the corresponding row. This is used in the entropy calculation for the corresponding bicluster. And 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
Ed Curry [email protected]
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