Rare disease-based scientific annotation knowledge graph.

Autor:	Zhu Q; Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, United States., Qu C; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, United States., Liu R; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, United States., Vatas G; GMG ArcData, LLC, Washington, DC, United States., Clough A; Data Decode, LLC, Washington, DC, United States., Nguyễn ÐT; Digital R&D Solutions, Pfizer, New York, NY, United States., Sid E; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, United States., Mathé E; Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, United States., Xu Y; Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences, Bethesda, MD, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2022 Aug 11; Vol. 5, pp. 932665. Date of Electronic Publication: 2022 Aug 11 (Print Publication: 2022).
DOI:	10.3389/frai.2022.932665
Abstrakt:	Rare diseases (RDs) are naturally associated with a low prevalence rate, which raises a big challenge due to there being less data available for supporting preclinical and clinical studies. There has been a 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 the 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 (KG), 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 KG, we propose to extend the current effort by expanding more RD-related publications and more other types of resources as a next step. Competing Interests: GV worked at HHS and is employed by GMG ArcData, LLC. AC worked at HHS and is employed by Data Decode, LLC. Ð-TN worked at NCATS and is employed by Pfizer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Zhu, Qu, Liu, Vatas, Clough, Nguyễn, Sid, Mathé and Xu.)
Databáze:	MEDLINE
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