Enhancing Omics Analyses Through Coalitional Games and Shapley Values.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Omics data analysis often relies on conventional statistical methods.
• Detecting subtle biological signals in high-dimensional datasets remains a challenge.
• There is a need for complementary approaches to enhance reproducibility and interpretability.

Purpose of the Study:

• To introduce a comprehensive methodology applying game theory to omics data analysis.
• To demonstrate the utility of coalitional games and Shapley values for transcriptomics.
• To improve the detection of biologically meaningful signals often missed by traditional methods.

Main Methods:

• Application of coalitional game theory and Shapley values to high-dimensional transcriptomics data.
• Development of a mathematical framework and implementation details for the proposed methodology.
• Evaluation of the approach's ability to identify cooperative gene distributions.

Main Results:

• The game theory approach successfully identifies biologically relevant signals.
• This method provides a complementary perspective to conventional statistical analyses.
• Improved detection of signals not explicitly modeled by standard techniques was observed.

Conclusions:

• Coalitional game theory offers a powerful tool for omics data analysis.
• The methodology enhances reproducibility and interpretability in transcriptomics research.
• This work opens new avenues for systems biology and precision medicine.