Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction

TL;DR

This paper introduces HCMGNN, a novel graph neural network model that predicts gene-microbe-disease associations by analyzing causal relationships within a heterogeneous graph, advancing understanding of complex biological interactions.

Contribution

The paper presents a new Heterogeneous Causal Metapath Graph Neural Network that captures multi-view causal relations among genes, microbes, and diseases for association prediction.

Findings

HCMGNN outperforms existing methods in GMD association prediction.

The model effectively addresses data sparsity issues.

Causal metapaths improve the interpretability of associations.

Abstract

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.