Structured Reviews for Data and Knowledge Driven Research

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://ngly1graph.org/">http://ngly1graph.org/</jats:ext-link>.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Source code and network data files are at: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/SuLab/ngly1-graph">https://github.com/SuLab/ngly1-graph</jats:ext-link> and <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/SuLab/bioknowledge-reviewer">https://github.com/SuLab/bioknowledge-reviewer</jats:ext-link>.</jats:p></jats:sec><jats:sec><jats:title>Contact</jats:title><jats:p><jats:email>asu@scripps.edu</jats:email></jats:p></jats:sec>

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

“…To date, the vast majority of KG construction algorithms have been developed in order to create more manageable representations of large free-text corpora (e.g. scientific articles) [9,10] , to derive novel associations between existing concepts [11,12] , and add evidence to existing systems or KGs [13,14] . While many data-driven KG construction methods have been developed, they remain largely unable to automatically construct KGs from multiple disparate data sources, combine KGs created by different systems, and collaborate or share KGs across institutions due to their inability to account for the use of different schemas, standards, and vocabularies [15] .…”