Abstract
Nowadays, microarray technology is available to generate a huge amount of information on gene expression. This information must be statistically processed and analyzed, in particular, to identify those genes which are useful for the diagnosis and prognosis of specific diseases. We discuss the possibility of applying game-theoretical tools, like the Shapley value, to the analysis of gene expression data.
Via a “truncation” technique, we build a coalitional game whose aim is to stress the relevance (“sufficiency”) of groups of genes for the specific disease we are interested in. The Shapley value of this game is used to select those genes which deserve further investigation. To justify the use of the Shapley value in this context, we axiomatically characterize it using properties with a genetic interpretation.
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The authors are grateful to two anonymous referees for their extremely helpful comments.
An earlier version of this paper was presented at the VI Spanish Meeting on Game Theory and Practice, July 12–14, 2004, Elche, Spain.
S. Moretti gratefully acknowledges the financial support of the EU project NewGeneris, European Union 6th FP (FOOD-CT-2005-016320).
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Moretti, S., Patrone, F. & Bonassi, S. The class of microarray games and the relevance index for genes. TOP 15, 256–280 (2007). https://doi.org/10.1007/s11750-007-0021-4
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DOI: https://doi.org/10.1007/s11750-007-0021-4