ArticleBriefings in bioinformatics2026
BHMnet: Bayesian high-dimensional mediation analysis with network information integration for correlated mediators.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
We consider identifying a small yet meaningful set of active mediators from a high-dimensional pool of potential mediators, commonly derived from "-omics" or imaging data. In these contexts, mediators are often correlated or exist network structures, which present unique opportunities to improve efficacy by using this valuable information. To this aim, we develop a Bayesian method that accommodates both high dimensionality and correlations among the mediators. Our approach flexibly learns the interconnection between the mediators while improving estimation accuracy by incorporating external knowledge about these relationships. Simulation studies demonstrate the effectiveness of the proposed method compared with alternative approaches. The analysis of the environmental toxicity data provides new insights into the intermediate effects of molecular-level traits.
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