J. Harry Caufield - KG-IDG: A FAIR Knowledge Graph for Illuminating the Druggable Genome
Channel: Biocuration Conference 2023
Date: 21/06/2023

*Session:* April 25 - Fair 2 - 02

*Abstract:* Knowledge graphs (KGs) are representations of heterogeneous entities and their diverse relationships. Though KGs combine intuitive data models with massive data collections, their application to domain-spanning questions in biomedicine is constrained by the effort required to consistently bridge gaps between a massive array of frequently-updating resources. We sought to develop a KG in the context of the NIH-sponsored Illuminating the Druggable Genome (IDG) project with this challenge in mind. IDG research is focused on improving our understanding of the properties and functions of proteins that are currently unannotated within three commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Accordingly, we designed KG-IDG to integrate otherwise uncoordinated sources of drug vs. target information, e.g., drug properties and target interactions from the DrugCentral resource; protein target details from the Target Central Resource Database; diseases/phenotypes from MONDO, OMIM, Orphanet, and the HPO; along with several other data sources and ontologies. The KG-IDG graph supports intensive graph-based machine learning methods for inference of novel relationships between drugs, proteins, and diseases. KG-IDG incorporates several concrete design elements to ease its application to specific research questions. All contents are built upon the Biolink Model, a data model purpose-built for flexible representation of biological associations in property graphs. All graph versions are assembled using the Knowledge Graph Exchange (KGX) platform, with all components following a highly modular and configurable data ingest and transformation pipeline. The greatest contribution KG-IDG makes to data FAIRness, however, is its adherence to reproducibility and open-source principles: all code, transformed data, and graphs are publicly available. All graph products are distributed in easily human-readable KGX TSV format. We feel that this overall approach allows KG-IDG to serve as the foundation for future KG construction efforts while also yielding interoperable, machine readable data resources. All code for assembling KG-IDG is available at https://github.com/Knowledge-Graph-Hub/kg-idg.