论文标题
医学知识图基于多跳机阅读理解的药物相互作用预测的质量图
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension
论文作者
论文摘要
药物 - 药物相互作用预测是分子生物学中的关键问题。通过医学实验观察药物相互作用的传统方法需要大量资源和劳动。本文提出了一种称为Medkgqa的医学知识图答案模型，该模型通过使用封闭域文献中的机器阅读理解理解并构建了开放域文档的药物蛋白质三重态的知识图，从而预测了药物毒品的相互作用。该模型使用实体嵌入图将图形中的药物蛋白靶标属性进行了矢量，并基于人体蛋白质靶标的代谢相互作用途径在药物和蛋白质实体之间建立了定向连接。这与多个外部知识保持一致，并将其应用于学习图神经网络。没有铃铛和口哨声，与Qangaroo Medhop数据集中的先前最先进的模型相比，该模型在药物相互作用预测的准确性方面提高了4.5％。实验结果证明了模型的效率和有效性，并验证了将外部知识整合在机器阅读理解任务中的可行性。
Drug-drug interaction prediction is a crucial issue in molecular biology. Traditional methods of observing drug-drug interactions through medical experiments require significant resources and labor. This paper presents a medical knowledge graph question answering model, dubbed MedKGQA, that predicts drug-drug interaction by employing machine reading comprehension from closed-domain literature and constructing a knowledge graph of drug-protein triplets from open-domain documents. The model vectorizes the drug-protein target attributes in the graph using entity embeddings and establishes directed connections between drug and protein entities based on the metabolic interaction pathways of protein targets in the human body. This aligns multiple external knowledge and applies it to learn the graph neural network. Without bells and whistles, the proposed model achieved a 4.5% improvement in terms of drug-drug interaction prediction accuracy compared to previous state-of-the-art models on the Qangaroo MedHop dataset. Experimental results demonstrate the efficiency and effectiveness of the model and verify the feasibility of integrating external knowledge in machine reading comprehension tasks.
