论文标题
旨在将特定于实体的知识图纳入预测药物相互作用
Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions
论文作者
论文摘要
从最近发布的各种预训练的语言模型（例如BERT，XLNET）获得的现成的生物医学嵌入已经证明了生物医学领域的各种自然理解任务（NLU）的最新结果（在准确性方面）。关系分类（RC）属于最关键的任务之一。在本文中，我们探讨了如何合并从知识图（KG）嵌入的生物医学实体（例如药物，疾病，基因）的领域知识，以预测文本语料库的药物毒用相互作用。我们提出了一种新方法Bertkg-DDI，以结合其与其他生物医学实体与域特异性生物Biobert嵌入基于嵌入的RC架构的相互作用获得的药物嵌入。在2013年Ddiextraction 2013语料库上进行的实验清楚地表明，该策略将其他基线体系结构提高了4.1％的宏F1得分。
Off-the-shelf biomedical embeddings obtained from the recently released various pre-trained language models (such as BERT, XLNET) have demonstrated state-of-the-art results (in terms of accuracy) for the various natural language understanding tasks (NLU) in the biomedical domain. Relation Classification (RC) falls into one of the most critical tasks. In this paper, we explore how to incorporate domain knowledge of the biomedical entities (such as drug, disease, genes), obtained from Knowledge Graph (KG) Embeddings, for predicting Drug-Drug Interaction from textual corpus. We propose a new method, BERTKG-DDI, to combine drug embeddings obtained from its interaction with other biomedical entities along with domain-specific BioBERT embedding-based RC architecture. Experiments conducted on the DDIExtraction 2013 corpus clearly indicate that this strategy improves other baselines architectures by 4.1% macro F1-score.
