In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology–Causal Activity Modelling, or GO–CAM). In this study, we explored whether the GO–CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural–Circuit Causal Activity Modelling (CeN–CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN–CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.

中文翻译：

---

秀丽隐杆线虫神经回路知识的语义表示

在现代生物学中，新知识产生得很快，这使得研究人员从大量原始出版物中有效获取和合成新信息面临挑战。为了解决这个问题，已经开发了以知识图的形式生成科学发现的机器可读表示的计算方法。这些表示可以将来自多篇论文和生物知识库的不同类型的实验数据整合到一个统一的数据模型中，为与已发表的知识进行交互的手动审查提供了补充方法。基因本体联盟 (GOC) 创建了一个语义建模框架，将单个功能基因注释扩展到代表生物过程的因果网络的结构化描述（基因本体-因果活动建模，或 GO-CAM）。在这项研究中，我们探讨了 GO-CAM 框架是否可以代表线虫模型线虫 [C. elegans] 中环境输入、神经回路和行为之间因果关系的知识。线虫神经回路因果活动模型（CeN-CAM）]。我们发现，通过对几个相关本体论的扩展，文献中关于产卵和二氧化碳（CO2）回避行为的神经回路基础的各种作者陈述可以用 CeN-CAM 忠实地表示。通过这个过程，我们能够为几类实验结果生成通用数据模型。我们还讨论了如何使用语义建模来对线虫连接组进行功能注释。因此，基于基因本体的语义建模有潜力支持神经生物学知识的各种机器可读表示。