Table_2_Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network.XLSX

More and more clinical observations have implied that microbes have great effects on human diseases. Understanding the relations between microbes and diseases are of profound significance for disease prevention and therapy. In this paper, we propose a predictive model based on the known microbe-disease associations to discover potential microbe-disease associations through integrating Learning Graph Representations and a modified Scoring mechanism on the Heterogeneous network (called LGRSH). Firstly, the similarity networks for microbe and disease are obtained based on the similarity of Gaussian interaction profile kernel. Then, we construct a heterogeneous network including these two similarity networks and microbe-disease associations’ network. After that, the embedding algorithm Node2vec is implemented to learn representations of nodes in the heterogeneous network. Finally, according to these low-dimensional vector representations, we calculate the relevance between each microbe and disease by utilizing a modified rule-based inference method. By comparison with three other methods including LRLSHMDA, KATZHMDA and BiRWHMDA, LGRSH performs better than others. Moreover, in case studies of asthma, Chronic Obstructive Pulmonary Disease and Inflammatory Bowel Disease, there are 8, 8, and 10 out of the top-10 discovered disease-related microbes were validated respectively, demonstrating that LGRSH performs well in predicting potential microbe-disease associations.

越来越多的临床观察结果证实，微生物对人类疾病具有显著影响。阐明微生物与疾病之间的关联，对于疾病的预防与治疗具有深远意义。本文提出一种命名为LGRSH的预测模型，通过整合异质网络（Heterogeneous network）上的图表示学习（Learning Graph Representations）与改进型评分机制，来挖掘潜在的微生物-疾病关联。首先，基于高斯相互作用轮廓核（Gaussian interaction profile kernel）相似性，分别构建微生物相似性网络与疾病相似性网络；随后，整合上述两类相似性网络与微生物-疾病关联网络，构建异质网络；接着，采用嵌入算法Node2vec学习异质网络中各节点的向量表示；最后，基于得到的低维向量表示，通过改进的基于规则的推理方法，计算每一对微生物与疾病之间的关联相关性。通过与LRLSHMDA、KATZHMDA及BiRWHMDA三种现有方法对比，本研究所提出的LGRSH模型表现更优。此外，在哮喘、慢性阻塞性肺疾病（Chronic Obstructive Pulmonary Disease）与炎症性肠病（Inflammatory Bowel Disease）的案例研究中，预测得到的Top10疾病相关微生物分别有8株、8株及10株得到了实验验证，充分证明LGRSH模型在潜在微生物-疾病关联预测任务中具有优异性能。