Sang Shengtian, Liu Xiaoxia, Chen Xiaoyu, Zhao Di
IEEE/ACM Trans Comput Biol Bioinform. 2022 May-Jun;19(3):1294-1301. doi: 10.1109/TCBB.2020.3003947. Epub 2022 Jun 3.
Nowadays, the amount of biomedical literatures is growing at an explosive speed, and much useful knowledge is yet undiscovered in the literature. Classical information retrieval techniques allow to access explicit information from a given collection of information, but are not able to recognize implicit connections. Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting literature. It could significantly support scientific research by identifying new connections between biomedical entities. However, most of the existing approaches to LBD are not scalable and may not be sufficient to detect complex associations in non-directly-connected literature. In this article, we present a model which incorporates biomedical knowledge graph, graph embedding, and deep learning methods for literature-based discovery. First, the relations between biomedical entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Second, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Third, a bidirectional Long Short-Term Memory (BLSTM) network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.
如今，生物医学文献的数量正以爆炸式速度增长，且文献中仍有许多有用的知识未被发现。传统的信息检索技术能够从给定的信息集合中获取明确的信息，但无法识别隐含的联系。基于文献的发现（LBD）的特点是在不相互作用的文献中揭示隐藏的关联。它可以通过识别生物医学实体之间的新联系来显著支持科学研究。然而，现有的大多数基于文献的发现方法都不可扩展，可能不足以检测非直接相关文献中的复杂关联。在本文中，我们提出了一种模型，该模型结合了生物医学知识图谱、图嵌入和深度学习方法用于基于文献的发现。首先，从生物医学摘要中提取生物医学实体之间的关系，然后利用这些获得的关系构建知识图谱。其次，应用图嵌入技术将知识图谱中的实体和关系转换到低维向量空间。第三，基于预训练图嵌入所表示的实体关联训练双向长短期记忆（BLSTM）网络。最后，将学习到的模型用于开放和封闭的基于文献的发现任务。实验结果表明，我们的方法不仅能够有效地发现实体之间的隐藏关联，还能够揭示相应的相互作用机制。这表明结合知识图谱和深度学习方法是捕捉文献中隐藏的实体之间潜在复杂关联的有效方法。
