Zentangler: A Multimodal Mediation Analysis Framework for Multiview Data

Thomas M. Menino Convention & Exhibition Center 

Multimodal artificial intelligence (AI) has advanced considerably over the past decade, making multimodal data integration increasingly central to biomedical research. However, most existing analytic approaches remain limited to association-based, single-modality modeling, and there is currently no unified framework for causal mediation analysis across biological layers.

To address this gap, we introduce a population-scale multimodal mediation framework for characterizing causal relationships across multiomics data. The framework accommodates both parallel mediation structures (e.g., X → M₁ → Y and X → M₂ → Y) and sequential pathways (e.g., X → M₁ → M₂ → Y), enabling systematic decomposition of cross-modal interactions and their influence on downstream outcomes.

We present Zentangler, a unified multimodal AI engine that integrates early, intermediate, and late fusion strategies, and supports continuous, binary, survival, and multiclass outcomes. This engine is embedded within a counterfactual mediation framework, allowing decomposition of total effects into natural direct and indirect components across complex mediation pathways.

Across large-scale microbiome multiomics datasets, including iHMP and FINRISK, Zentangler outperforms existing single-modality approaches and reveals biologically meaningful cross-modal relationships. Synthetic benchmarking further demonstrates its improved accuracy and robustness. The method is implemented as an open-source R/Bioconductor package, available at: https://github.com/himelmallick/Zentangler/

Multimodal AI

Causal Mediation Analysis

Multimodal Integration

Microbiome

Multiomics