Rare disease-based scientific annotation knowledge graph - Zhu, Qian; Qu, Chunxu; Liu, Ruizheng; Vatas, Gunjan; Clough, Andrew; Nguyễn, Ðắc-Trung; Sid, Eric; Mathé, Ewy; Xu, Yanji (Frontiers in Artificial Intelligence, 2022)
Rare diseases (RD) are naturally associated with low prevalence rate, which raises a big challenge due to less data available for supporting preclinical and clinical studies. There has been vast improvement in our understanding of RD, largely owing to advanced big data analytic approaches in genetics/genomics. Consequently, a large volume of RD related publications has been accumulated in recent years, which offers opportunities to utilize these publications for accessing full spectrum of the scientific research and supporting further investigation in RD. In this study, we systematically analyzed, semantically annotated and scientifically categorized RD related PubMed articles, and integrated those semantic annotations in a knowledge graph, which is hosted in Neo4j based on a predefined data model. With the successful demonstration of scientific contribution in RD via the case studies performed by exploring this knowledge graph, we propose to extend the current effort by expanding more RD related publications and more other types of resources as a next step.