# Enhancing Omics Analyses Through Coalitional Games and Shapley Values

## TL;DR

This paper introduces a game theory approach to omics data analysis, improving the detection of meaningful biological signals.

## Contribution

The novelty lies in applying coalitional games and Shapley values to enhance omics data analysis.

## Key findings

- The method improves the detection of biologically meaningful signals in transcriptomics data.
- It enhances reproducibility and interpretability in omics research.
- The approach offers new perspectives in systems biology and precision medicine.

## Abstract

We describe a comprehensive methodology for the application of game theory to omics data analysis, with a particular focus on coalitional games and Shapley values. This approach evaluates the cooperative distribution of genes within high-dimensional transcriptomics datasets, providing a complementary perspective to conventional statistical methods. We present the mathematical framework, implementation details, and references for applications that demonstrate its ability to improve the detection of biologically meaningful signals that may not be explicitly modeled by many conventional statistical methods. Our results highlight the potential of coalitional game theory as a powerful tool for enhancing reproducibility and interpretability in omics research, opening new perspectives in systems biology and precision medicine.