ArticleBMC bioinformatics2008
Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 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
- Article
- Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection inBiology · 2023Article
- Redundancy-aware unsupervised ranking based on game theory: Ranking pathways in collections of gene sets.PloS one · 2023Article
- Systemic CLIP-seq analysis and game theory approach to model microRNA mode of binding.Nucleic acids research · 2021Article
- Game theoretic centrality: a novel approach to prioritize disease candidate genes by combining biological networks with the Shapley value.BMC bioinformatics · 2020Article
- Coalitional Game Theory Facilitates Identification of Non-Coding Variants Associated With Autism.Biomedical informatics insights · 2019Article
- Coalitional game theory as a promising approach to identify candidate autism genes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Global transcriptome analysis reveals small RNAs affecting Neisseria meningitidis bacteremia.PloS one · 2015Article
- Translational Meta-analytical Methods to Localize the Regulatory Patterns of Neurological Disorders in the Human Brain.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
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8 authors.
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Abstract
backgroundIn gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions.
resultsIn this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability.
conclusionCASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways.
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