Motivation Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships. Results In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite–disease interactions. In computational experiments, HGLMF accurately predicted the metabolite–disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies. Availability and implementation The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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基于超图的逻辑矩阵分解用于代谢物-疾病相互作用预测

动机功能相关代谢物是细胞调节的终端产物，与复杂疾病密切相关。疾病相关代谢物的鉴定对于疾病的诊断、预防和治疗至关重要。然而，大多数现有的计算方法通过计算成对关系来构建网络，这不适合挖掘高阶关系。结果 在这项研究中，我们提出了一种基于超图的逻辑矩阵分解 HGLMF 的新方法，以预测代谢物与疾病之间的潜在相互作用。首先，提取代谢物的分子结构和基因关联以及疾病的层次结构和GO功能注释，构建代谢物和疾病的各种相似性度量。下一个，根据完整的交互网络计算代谢物（或疾病）的核邻域相似度。其次，分别融合代谢物和疾病的多个网络，构建代谢物和疾病的超图结构。最后，提出了基于超图的逻辑矩阵分解来预测潜在的代谢物-疾病相互作用。在计算实验中，HGLMF 准确地预测了代谢物与疾病的相互作用，并且比其他最先进的方法表现得更好。此外，HGLMF 可用于预测新的代谢物（或疾病）。正如案例研究所表明的那样，所提出的方法可以发现新的与疾病相关的代谢物，这已在现有研究中得到证实。可用性和实施​​代码和数据集可在以下位置获得：https：//github.com/Mayingjun20179/HGLMF。补充信息 补充数据可在 Bioinformatics 在线获取。