Scientific evidence based rare disease research discovery with research funding data in knowledge graph

Abstract

Background Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study. Methods To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery. Results Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research. Conclusion We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases.

Building a knowledge graph to enable precision medicine

“…Although SPOKE is limited to 137 diseases and lacks multimodal connections between textual clinical guidelines and tabular molecular data, it has enabled many precision medicine efforts, including overlaying individual patient information onto the SPOKE’s graph 35 . Another knowledge graph focused exclusively on rare diseases, Genetic and Rare Diseases Information Center (GARD) 34 , has advanced understanding of unmet medical needs and evidence-based studies for patients with under-diagnosed diseases 66 , 67 . Most recently, a White House initiative led the development of the COVID-19 Open Research Dataset (CORD-19) 68 .…”

Rare disease-based scientific annotation knowledge graph

“…Since authors and their affiliations are included in this KG, a collaborative network can be established among authors who are co-authors in the same articles or share similar research interests based on PubTator annotations. As a scientific evidence repository, it supports multi-KG integration with the NGKG (Zhu et al, 2020 ) ( https://disease.ncats.io/browser/ ) and the grant-based KG ( http://grants4rd.ncats.io:7474/browser/ ) (Zhu et al, 2021 ) based on their shared nodes including Disease nodes (i.e., GARD diseases) and Article nodes (i.e., PMIDs) for scientific evidence-based research study in RD.…”

“…(https://disease.ncats.io/ browser/) and the grant-based KG (http://grants4rd.ncats.io: 7474/browser/)(Zhu et al, 2021) based on their shared nodes including Disease nodes (i.e., GARD diseases) and Article nodes (i.e., PMIDs) for scientific evidence-based research study in RD.…”

Recommendations from the Medical Education Editor

“…Clinical research is difficult in patients with rare diseases, and both pathophysiology and response to treatments can be poorly understood 9 . Muller et al have provided an excellent review of thoracic involvement in IgG4‐RD.…”

“…Clinical research is difficult in patients with rare diseases, and both pathophysiology and response to treatments can be poorly understood. 9 Muller et al have provided an excellent review of thoracic involvement in IgG4-RD. A review article such as this is an essential contribution to the literature and a valuable resource for clinicians and researchers.…”