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.

翻译：近期人类医学对微生物的关注凸显了其在疾病遗传框架中的潜在作用。为解析基因、微生物与疾病之间复杂的相互作用，基因-微生物-疾病（GMD）关联的计算预测至关重要。现有方法主要处理基因-疾病和微生物-疾病关联，但更为复杂的三元GMD关联仍未得到充分探索。本文提出一种异质因果元路径图神经网络（HCMGNN）来预测GMD关联。HCMGNN通过两两关联构建连接基因、微生物和疾病的异质图，并利用六种预定义的因果元路径提取有向因果子图，从而促进对三类实体间因果关系的多视角分析。在每个子图中，我们采用因果语义共享消息传递网络进行节点表示学习，并结合注意力融合方法整合这些表示以预测GMD关联。大量实验表明，HCMGNN能有效预测GMD关联，并通过增强图的语义和结构解决关联稀疏性问题。