  The recent focus on microbes in human medicine highlights their potential
role in the genetic framework of diseases. To decode the complex interactions
among genes, microbes, and diseases, computational predictions of
gene-microbe-disease (GMD) associations are crucial. Existing methods primarily
address gene-disease and microbe-disease associations, but the more intricate
triple-wise GMD associations remain less explored. In this paper, we propose a
Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD
associations. HCMGNN constructs a heterogeneous graph linking genes, microbes,
and diseases through their pairwise associations, and utilizes six predefined
causal metapaths to extract directed causal subgraphs, which facilitate the
multi-view analysis of causal relations among three entity types. Within each
subgraph, we employ a causal semantic sharing message passing network for node
representation learning, coupled with an attentive fusion method to integrate
these representations for predicting GMD associations. Our extensive
experiments show that HCMGNN effectively predicts GMD associations and
addresses association sparsity issue by enhancing the graph's semantics and
structure.
