面向数据和知识驱动研究的结构化综述。

Structured reviews for data and knowledge-driven research.

机构信息

Department of Integrative Structural and Computational Biology, Scripps Research, 10550 N Torrey Pines Rd. La Jolla, CA 92037, USA.

Department of Biochemistry and Molecular Biology, Oregon Health and Science University, 3181 SW Sam Jackson Parkway, Portland, OR 97239, USA.

出版信息

Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baaa015.

DOI:10.1093/database/baaa015

PMID:32283553

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7153956/

Abstract

UNLABELLED

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.

摘要

目的

假设生成是研究的关键步骤，也是罕见病领域的基石。当这些假设基于迄今为止已知的全部知识时，研究将最有效。系统评价文章在生物医学中常用于总结现有知识并将实验数据置于上下文中。但是，综述文章中包含的信息通常仅以自由文本的形式表达，这很难进行计算处理。研究人员在没有无缝集成和访问的情况下，努力在几个孤立的环境中导航、收集和重新组合先前的知识。这种缺乏结构化信息框架的情况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，我们构建了一个专门针对 NGLY1 缺乏症的结构化综述文章，这是一种 2012 年首次报道的超罕见遗传疾病。我们将这个结构化综述表示为一个知识图，并将其存储在 Neo4j 数据库中，以简化网络的传播、查询和可视化。与自由文本相比，这种结构化综述更能促进可发现性、可访问性、互操作性和可重用性（FAIR）的原则。与 NGLY1 缺乏症领域的专家合作，我们展示了如何使用这种资源提高假设生成的效率和全面性。我们还开发了一个读写接口，允许领域专家将 FAIR 结构化知识贡献给这个社区资源。与传统的自由文本综述文章不同，这种结构化综述作为一个由人类维护的、可用于计算分析的活知识图存在。最后，我们将这个工作流程概括为可应用于其他领域的模块化和可重复使用的组件。这个以 NGLY1 缺乏症为重点的网络在 http://ngly1graph.org/ 上公开可用。