近日，实验室博士生李楠关于生物医学知识图谱表示学习的研究成果被生物信息学领域领期刊Journal of Biomedical Informatics (JBI)录用。JBI属于JCR一区、CCF推荐C类期刊，影响因子为4.5。

题目： Hyperbolic Hierarchical Knowledge Graph Embeddings for Biological Entities

摘要：Predicting relationships between biological entities can greatly benefit important biomedical problems. Previous studies have attempted to represent biological entities and relationships in Euclidean space using embedding methods, which evaluate their semantic similarity by representing entities as numerical vectors. However, the limitation of these methods is that they cannot prevent the loss of latent hierarchical information when embedding large graph-structured data into Euclidean space, and therefore cannot capture the semantics of entities and relationships accurately. Hyperbolic space is better suited for hierarchical modeling than Euclidean space. This is because hyperbolic spaces exhibit negative curvature, causing distances to grow exponentially as they approach the boundary. In this paper, we propose HEM, a hyperbolic hierarchical knowledge graph embedding model to generate vector representations of bio-entities. By encoding the entities and relations in the hyperbolic space, HEM can capture latent hierarchical information and improve the accuracy of biological entity representation. Notably, HEM can preserve rich information with a low dimension compared with the methods that encode entities in Euclidean space. Furthermore, we explore the performance of HEM in protein-protein interaction prediction and gene-disease association prediction tasks.

挖掘生物实体之间的潜在关系对生物医学领域有着重要的意义。近年来，已有多种表示学习方法尝试在欧氏空间中将生物实体表示为向量形式，用于评估实体之间的语义相似性。然而，这类方法的局限性在于当对大规模的知识图谱进行建模时，不可避免的造成潜在层次信息的丢失。当模型不能完全捕获层级语义信息时，可能会导致实体的语义表示不准确。与欧式空间相比，双曲空间在建模层级信息上具有天然的优势。在本文中，我们提出了一种结合层次化信息的知识图谱表示学习模型（HEM），通过在双曲空间中对知识图谱建模，HEM可以捕获潜在的层次信息，提高生物实体表示的准确性。最后，我们在蛋白质-蛋白质交互预测和基因-疾病关联预测两个任务对HEM的性能进行验证。实验结果表明，HEM的性能始终优于其他对比方法。