MiMed

MiMed performs mediation analysis of microbiome data to identify microbial taxa and community-level features that mediate associations between exposures and health outcomes.

Key Features:

• Ecosystem-Level Analysis: Evaluates the microbiome as a whole using ecological measures such as alpha- and beta-diversity indices.
• Taxa-Specific Analysis: Performs detailed analyses of individual microbial taxa at phylum, class, order, family, genus, and species levels using various data normalization methods.
• Covariate-Adjusted Analysis: Supports covariate adjustment to control for potential confounders such as age and gender.
• Statistical Inference for Mediation: Estimates mediation effects and conducts significance testing for identified mediators.
• Data Processing and Visualization: Provides modules for data processing and generation of graphical representations of results.
• Integration of Artificial Intelligence: Employs ChatGPT to search existing literature on microbial taxa identified as significant mediators.

Scientific Applications:

• Microbiome-mediated exposure–outcome analysis: Identifies microbial mediators linking exposures and health outcomes, exemplified by analysis of the oral microbiome in subgingival niches between e-cigarette smoking and gingival inflammation.
• Microbiome ecosystem characterization: Assesses community-level diversity and taxon-specific contributions to understand complex interactions within the microbiome and their implications for health and disease.

Methodology:

Calculates alpha- and beta-diversity indices; conducts taxon-level analyses at phylum, class, order, family, genus, and species levels with data normalization methods; performs covariate-adjusted analyses controlling for factors such as age and gender; estimates mediation effects and runs significance testing; uses ChatGPT to search literature on taxa identified as significant mediators.