Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
The Shapley and Banzhaf values in microarray games
Computers and Operations Research
Statistical analysis of the Shapley value for microarray games
Computers and Operations Research
An overview of recent applications of Game Theory to bioinformatics
Information Sciences: an International Journal
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In [16] a methodology based on Game Theory for the analysis of gene expression data is studied. Roughly speaking, the starting point is the observation of a 'picture' of gene expressions in a sample of cells under a biological condition of interest, for example a tumor. Then, Game Theory plays a primary role to quantitatively evaluate the relevance of each gene in regulating or provoking the condition of interest, taking into account the observed relationships in all subgroups of genes. In this paper, an alternative model based on minimum cost spanning tree representation of gene expression data has been introduced. One of the main characteristics of this model is the possibility to avoid the dichotomization technique required for microarray games introduced in [17].