ArticleApplied network science2018
An application of the Shapley value to the analysis of co-expression networks.
Article in Applied network science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Enhancing Omics Analyses Through Coalitional Games and Shapley Values.Methods and protocols · 2026Article
- Enhancing Transcriptomic Insights into Neurological Disorders Through the Comparative Analysis of Shapley Values.Current issues in molecular biology · 2024Article
- Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.Scientific reports · 2024Article
- Article
- Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.PloS one · 2023Article
- Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer.Scientific reports · 2022Article
- Mathematical indices for the influence of risk factors on the lethality of a disease.Journal of mathematical biology · 2021Article
- Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network.Diagnostics (Basel, Switzerland) · 2020Article
- Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory.Iranian journal of biotechnology · 2020Article
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Authors and funding
4 authors.
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Abstract
We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex biological pathways of interest. An application of the method to the analysis of gene expression data from microarrays is presented, as well as a comparison with classical centrality indices. Finally, making further assumptions about the a priori importance of genes, we combine the game theoretic model with other techniques from cluster analysis.
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