A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.
|Author||Martin Guerrero [aut], Carlos Catania [cre]|
|Bioconductor views||Classification Clustering GeneExpression Survival Transcription|
|Maintainer||Carlos Catania <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
|Package repository||View on Bioconductor|
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