Hypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists. To better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/. AVAILABILITY AND IMPLEMENTATION Database URL: http://ngly1graph.org/. Network data files are at: https://github.com/SuLab/ngly1-graph and source code at: https://github.com/SuLab/bioknowledge-reviewer. CONTACT asu@scripps.edu.

中文翻译：

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对数据和知识驱动型研究进行结构化审查。

假设的产生是研究的关键步骤，也是罕见疾病领域的基石。当这些假设基于迄今为止已知的全部知识时，研究是最有效的。系统评价文章通常用于生物医学中，以总结现有知识并根据实验数据进行上下文描述。但是，评论文章中包含的信息通常仅以自由文本表示，这很难在计算中使用。由于先前的知识分散在多个孤岛中，而没有无缝的集成和访问，因此研究人员难以导航，收集和重新混合。缺乏结构化的信息框架阻碍了实验和计算科学家的研究。为了更好地组织知识和数据，我们撰写了一篇结构化的评论文章，专门针对NGLY1缺陷，一种超稀有遗传病，于2012年首次报道。我们将这种结构化评价表示为知识图谱，然后将该知识图谱存储在Neo4j数据库中，以简化网络的传播，查询和可视化。相对于自由文本，此结构化审查更好地促进了可发现性，可访问性，互操作性和可重用性（FAIR）的原则。与NGLY1缺陷领域专家合作，我们演示了该资源如何提高假设生成的效率和综合性。我们还开发了一个读写界面，使领域专家可以为该社区资源贡献FAIR结构化知识。与传统的自由文本评论文章不同，这种结构化的审查是一个实时的知识图，由人类策划并可以进行计算分析。最后，我们已将此工作流程概括为可应用于其他领域的模块化和可重用组件。这个以NGLY1缺陷为重点的网络可从http://ngly1graph.org/上公开获得。可用性和实现数据库URL：http://ngly1graph.org/。网络数据文件位于：https://github.com/SuLab/ngly1-graph，源代码位于：https://github.com/SuLab/bioknowledge-reviewer。请联系asu@scripps.edu。组织/。网络数据文件位于：https://github.com/SuLab/ngly1-graph，源代码位于：https://github.com/SuLab/bioknowledge-reviewer。请联系asu@scripps.edu。组织/。网络数据文件位于：https://github.com/SuLab/ngly1-graph，源代码位于：https://github.com/SuLab/bioknowledge-reviewer。请联系asu@scripps.edu。