School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190 China; College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA.
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190 China.
Methods. 2022 Jul;203:152-159. doi: 10.1016/j.ymeth.2022.02.002. Epub 2022 Feb 15.
Drug-drug interactions (DDIs) aim at describing the effect relations produced by a combination of two or more drugs. It is an important semantic processing task in the field of bioinformatics such as pharmacovigilance and clinical research. Recently, graph neural networks are applied on dependency graph to promote the performance of DDI extraction with better semantic representations. However, current method concentrates more on first-order dependency relations and cannot discriminate the connected nodes properly. To better incorporate the dependency relations and improve the representations, we propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work. Specifically, the dependency graph is enhanced with some potential long-range words to complete the semantic information and fit the aggregation process of graph neural networks. And graph attention mechanism is adopted to further improve word representation by discriminating the connected nodes according to the specific task. Numerical experiments on DDIExtraction 2013 corpus, the benchmark corpus for this domain, demonstrate the superiority of our proposed method.
药物-药物相互作用（DDI）旨在描述两种或多种药物联合使用产生的影响关系。它是药物警戒和临床研究等生物信息学领域的一项重要语义处理任务。最近，图神经网络已应用于依存关系图，以通过更好的语义表示来提高 DDI 提取的性能。然而，当前的方法更侧重于一阶依存关系，无法正确区分连接的节点。为了更好地结合依存关系并改进表示，我们在这项工作中提出了一种名为“使用增强型依存关系图和注意力机制的药物-药物相互作用提取”的新型 DDI 提取方法。具体来说，通过添加一些潜在的远程单词来增强依存关系图，以完成语义信息并适应图神经网络的聚合过程。并且采用图注意力机制，根据特定任务区分连接的节点，进一步提高单词表示。在 DDIExtraction 2013 语料库（该领域的基准语料库）上的数值实验证明了我们提出的方法的优越性。
