Problem
Research questions and friction points this paper is trying to address.
Reactome
semantic hierarchy
textual metadata
biological relationships
Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings
🤖 AI Summary
该研究使用句子转换器模型和图重建算法处理Reactome中的文本元数据，以解决生物知识库手动整理的可扩展性问题，并成功推断出专家定义的路径层级结构。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
SPECTER2
agglomerative nesting algorithm
graph reconstruction algorithm
semantic hierarchy
textual metadata
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