论文标题
分析COVID-19的药物重新利用知识图
Analysis of Drug repurposing Knowledge graphs for Covid-19
论文作者
论文摘要
知识图（kg）用于代表实体之间的实体和结构关系。该表示形式可用于解决复杂的问题，例如建议系统和问答。在这项研究中，通过使用重新利用知识图（DRKG）提出了一组COVID-19的候选药物。 DRKG是使用大量开源生物医学知识构建的生物知识图，以了解化合物的机制和相关的生物学功能。使用知识图嵌入模型以及神经网络以及与注意力相关的模型学习节点和关系嵌入。不同的模型用于通过更改模型的目标来获取节点嵌入。这些嵌入后来用于预测候选药物是否有效治疗疾病，或者药物与与疾病相关的蛋白质结合的可能性，该蛋白可以模拟为两个节点之间的链接预测任务。根据MR，MRR和HITS@3，Rescal在测试数据集上表现出了最好的表现。
Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph constructed using a vast amount of open source biomedical knowledge to understand the mechanism of compounds and the related biological functions. Node and relation embeddings are learned using knowledge graph embedding models and neural network and attention related models. Different models are used to get the node embedding by changing the objective of the model. These embeddings are later used to predict if a candidate drug is effective to treat a disease or how likely it is for a drug to bind to a protein associated to a disease which can be modelled as a link prediction task between two nodes. RESCAL performed the best on the test dataset in terms of MR, MRR and Hits@3.
