发表机构
Istituto per le Applicazioni del Calcolo Mauro Picone, Italian National Research Council; Università Campus Bio-Medico di Roma; UniCamillus-Saint Camillus International University of Health Sciences（意大利国家研究委员会 Mauro Picone 应用计算研究所; 罗马校园生物医学大学; 圣卡米勒斯国际健康科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用句子Transformer模型SPECTER2等技术，通过Reactome的文本元数据重构语义层级结构，证实可借助专家编写的文本元数据推断通路的全局层级结构，为生物知识库的自动扩展提供了可行方法。
AI 中文摘要
诸如Reactome这类生物知识库提供了高质量的通路，其中包含生物元素的关系和文本描述（元数据）。这类通路的质量由人工审核保障，但这也带来了显著的可扩展性挑战。最近，人们提出了许多自然语言处理工具来应对这一问题，它们利用文本信息自动扩展生物知识库。然而，迄今为止，很少有研究探讨文本描述之间的关系是否反映了高阶生物关系。本研究探讨Reactome中人类编写的描述是否可用于推断专家定义的全局层级结构。为验证这一点，我们从Reactome中提取了人属通路及其反应的层级结构（Reactome层级结构），并结合句子Transformer模型SPECTER2、改进的凝聚聚类算法和图重构算法，利用文本元数据重构了语义层级结构。定量分析（拉普拉斯谱距离和自举法）和定性分析（全局拓扑度量）证实了我们的假设，表明通路的全局层级结构可通过专家文本元数据推断得出。
英文摘要
Biological knowledgebases like Reactome provide high-quality pathways that include biological elements' relationships and textual descriptions (metadata). The quality of such pathways is granted by manual curation, that presents, however, significant scalability challenges. Lately, numerous NLP tools have been proposed to cope with this issue, leveraging textual information to automatically expand biological knowledgebases. However, little exploration has been done so far to assess whether relationships among textual descriptions mirror higher order biological relationships. This study explores whether human-written descriptions in Reactome can be used to infer the experts' defined global hierarchical structure. To test this, we extracted from Reactome the Homo Sapiens hierarchy of pathways and their reactions (Reactome Hierarchy), and used textual metadata to reconstruct a Semantic Hierarchy, combining a sentence transformer model (SPECTER2) with a modified agglomerative nesting algorithm and a graph reconstruction algorithm. Quantitative (Laplacian Spectral Distance and Bootstrapping) and qualitative (global topological metrics) analyses confirm our hypothesis and indicate that the global hierarchical structure of pathways can be inferred by experts textual metadata.
CommentsAccepted at the CIBB 2026 conference (https://cibb2026.teralab.ai/)
