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Found 3 papers, 2 papers with code
Redundancy-aware unsupervised rankings for collections of gene sets
The rankings can be used to reduce the dimension of collections of gene sets, such that they show lower redundancy and still a high coverage of the genes.
Redundancy-aware unsupervised ranking based on game theory -- application to gene enrichment analysis
However, we believe that the rankings proposed are of use in bioinformatics to increase interpretability of the gene sets collections and a step forward to include redundancy into Shapley values computations.
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.
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