用专家描述文本推断生物通路层级结构，验证了文本蕴含深层知识。
Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings
- 用SPECTER2+改进聚类算法，从文本描述重建通路语义层级
- 定量与定性分析均证实文本可还原专家定义的全局结构
- 适合生物信息学与大模型融合研究者参考
生物知识库如Reactome提供高质量通路数据，包含生物元素间的关系及文本描述（元数据）。这些通路的质量源于人工注释，但面临显著可扩展性挑战。近年来，众多NLP工具被提出以利用文本信息自动扩展知识库。然而，现有研究极少探讨文本描述间的关联是否反映更高阶的生物学关系。本研究探索人类编写的Reactome描述能否用于推断专家定义的全局层级结构。为此，我们提取了人类基因组（Homo Sapiens）的通路与反应层级（Reactome Hierarchy），并基于文本元数据重构语义层级结构，结合句子嵌入模型SPECTER2、改进的凝聚聚类算法及图重构算法。定量分析（拉普拉斯谱距离与自助法）和定性分析（全局拓扑指标）均支持假设，表明通过专家文本描述可有效推断通路的全局层级结构。
原文摘要 · Abstract (English)
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.
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