49: Prior-aware learning for high-dimensional mediation analysis of molecular data

Thomas M. Menino Convention & Exhibition Center 

High-dimensional mediation analyses of molecular data often ignore existing biological knowledge, despite the availability of databases describing molecular interactions, pathway memberships, and other functional relationships. We propose PALM (Prior-Aware Learning for Mediation), a graph learning framework that combines biological priors with data-driven empirical correlations to improve mediator effect estimation when any single source of prior information is incomplete or noisy. PALM fits a joint mediation model including all molecular candidates and uses a graph-based penalty to encourage related molecules to have similar effects. Rather than relying on one predefined molecular network, PALM learns a combined relationship graph from multiple complementary prior sources and incorporates this learned structure into the mediation model. By borrowing strength across connected mediators, PALM improves the stability and power of estimating indirect effects. Simulation studies show improved identification of true mediating signals across a range of heterogeneous prior-information settings compared with existing regularization methods. We apply PALM to metabolomics data from the Framingham Heart Study to illustrate its utility.

Mediation analysis

High-dimensional data

Graph learning

Prior knowledge integration 

Section on Statistics in Epidemiology