Towards FAIRer Biological Knowledge Networks Using a Hybrid Linked Data and Graph Database Approach

Abstract

The speed and accuracy of new scientific discoveries - be it by humans or artificial intelligence - depends on the quality of the underlying data and on the technology to connect, search and share the data efficiently. In recent years, we have seen the rise of graph databases and semi-formal data models such as knowledge graphs to facilitate software approaches to scientific discovery. These approaches extend work based on formalised models, such as the Semantic Web. In this paper, we present our developments to connect, search and share data about genome-scale knowledge networks (GSKN). We have developed a simple application ontology based on OWL/RDF with mappings to standard schemas. We are employing the ontology to power data access services like resolvable URIs, SPARQL endpoints, JSON-LD web APIs and Neo4j-based knowledge graphs. We demonstrate how the proposed ontology and graph databases considerably improve search and access to interoperable and reusable biological knowledge (i.e. the FAIRness data principles).

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

“…Another matter is how to express them properly. Graph databases are a suitable tool for this purpose that has been demonstrated to be an efficient and convenient way to store and explore metabolic networks [36,37]. Here, we used a proposed graph database (2Path-Sesquiterpenes) to store and enrich the simulation data with experimental evidence related to the predicted sesquiterpenes, the Scenarios.…”

Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks

“…Thus they are a potentially valuable resource, which can be leveraged more efficiently if the data are provided with a comprehensive and consistent data schema can support the FAIR Guiding Principles for scientific data management [37] and facilitate the exchange and interoperability. Graph databases are a suitable tool for this purpose that has been demonstrated to be an efficient and convenient way to store and explore metabolic networks [38,39]. Here we use a graph database to enriches simulation data with experimental evidence related to the predicted sesquiterpenes.…”

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

“…We have previously described our approaches to build genome-scale KGs (Hassani-Pak et al, 2016) , to extend KGs with novel gene-phenotype relations from the literature (Hassani-Pak et al, 2010) , to publish KGs as standardised and interoperable data based on FAIR principles (Brandizi et al, 2018a) and to visualise biological knowledge networks in an interactive web application (Singh et al, 2018) . Our data integration approach to build KGs is based on an intelligent data model with just enough semantics to capture complex biological relationships between genes, traits, diseases and many more information types derived from curated or predicted information sources ( Figure 1 ).…”