LLM-Assisted Nutrition-Disease Knowledge Graph Construction and Multi-View Fusion Framework for Link Prediction

The critical role of nutrients in intervening in human diseases has made a deeper exploration of microbial metabolic processes greatly needed. Bacteriophages serve as key regulators in maintaining microecological balance. Existing studies have revealed that nutrients promote bacteriophage production in gut bacteria, subsequently affecting bacterial abundance and influencing disease development. However, current research lacks systematic integration of the relationships among bacteriophages, nutrition, gut bacteria, and diseases, which partially limits our understanding of the intervention mechanisms between nutrition and diseases. To address this, we constructed a novel knowledge graph, KGNBVD, focusing on the interconnections among dietary nutrition, gut bacteria, bacteriophages, and human diseases by harnessing large-scale biomedical literature and the promptdriven capabilities of large language models (lLMs). Additionally, to elucidate the mechanistic role of bacteriophages in linking nutrients and human diseases, we proposed a multi-view fusion approach that fully explore the features of KGNBVD. Extensive experimental results demonstrated that our method outperforms state-of-the-art methods in predicting four-entity interactions with AUC values of 96.20 % and $\mathbf{F 1}$ scores of $\mathbf{8 9. 7 2 \%}$. Moreover, results of case study validated that our method can identify high-probability candidate entities for previously unseen entity interactions, effectively predicting potential interactions between nutrition and human diseases.