Inferring microbe-metabolite interactions by heterogeneous network fusion based on graph convolution network

Pei Li, Ying Xiao, Weizhong Zhao, Xingpeng Jiang, Xianjun Shen

Abstract: Inferring microbe-metabolite interactions is conducive to understand how microbes affect human health, and specific microbial metabolites can be used as biomarkers for the diagnosis and treatment. Most of the existing methods only utilize the known mechanisms between microbiome and metabolome, while ignore the intragroup biological interactions(including microbe-microbe correlations and metabolite-metabolite correlations). In this paper, we propose a microbe-metabolite heterogeneous network fusion model based on graph convolution network (MMHNF) for inferring microbe-metabolite interactions. The proposed model not only utilizes the common properties of microbe-metabolite interactions, but also applies the properties of microbe-microbe and metabolite-metabolite correlation networks. In addition, it can learn low-dimensional and effective feature representations from multisource heterogeneous networks by applying the graph convolution network. Comprehensive experiments are performed on both simulated and experimental data, and the results show that MMHNF outperforms selected baselines. Moreover, results of case studies on inflammatory bowel disease (IBD) demonstrate further the effectiveness of the proposed model.