de Bono Bernard, Gillespie Tom, Surles-Zeigler Monique C, Kokash Natallia, Grethe Jeff S, Martone Maryann
Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Department of Neuroscience, University of California, San Diego, San Diego, CA, United States.
Front Physiol. 2022 Apr 25;13:795303. doi: 10.3389/fphys.2022.795303. eCollection 2022.
We present (i) the ApiNATOMY workflow to build knowledge models of biological connectivity, as well as (ii) the ApiNATOMY TOO map, a topological scaffold to organize and visually inspect these connectivity models in the context of a canonical architecture of body compartments. In this work, we outline the implementation of ApiNATOMY's knowledge representation in the context of a large-scale effort, SPARC, to map the autonomic nervous system. Within SPARC, the ApiNATOMY modeling effort has generated the SCKAN knowledge graph that combines connectivity models and TOO map. This knowledge graph models flow routes for a number of normal and disease scenarios in physiology. Calculations over SCKAN to infer routes are being leveraged to classify, navigate and search for semantically-linked metadata of multimodal experimental datasets for a number of cross-scale, cross-disciplinary projects.
我们展示了 (i) 用于构建生物连通性知识模型的ApiNATOMY工作流程,以及 (ii) ApiNATOMY TOO图谱,这是一种拓扑支架,用于在身体腔室的规范架构背景下组织和可视化检查这些连通性模型。在这项工作中,我们概述了ApiNATOMY知识表示在大规模项目SPARC(自主神经系统图谱绘制项目)中的实施情况。在SPARC项目中,ApiNATOMY建模工作生成了结合连通性模型和TOO图谱的SCKAN知识图谱。该知识图谱对生理学中许多正常和疾病情况的流动路径进行建模。通过对SCKAN进行计算以推断路径,正被用于为多个跨尺度、跨学科项目对多模态实验数据集的语义链接元数据进行分类、导航和搜索。