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 t...

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Autores principales:	Qian Zhu, Ðắc-Trung Nguyễn, Timothy Sheils, Gioconda Alyea, Eric Sid, Yanji Xu, James Dickens, Ewy A. Mathé, Anne Pariser
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/6436e90bae0b4f9dbe8bc63d5bd9cd97
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spelling	oai:doaj.org-article:6436e90bae0b4f9dbe8bc63d5bd9cd972021-11-21T12:26:11ZScientific evidence based rare disease research discovery with research funding data in knowledge graph10.1186/s13023-021-02120-91750-1172https://doaj.org/article/6436e90bae0b4f9dbe8bc63d5bd9cd972021-11-01T00:00:00Zhttps://doi.org/10.1186/s13023-021-02120-9https://doaj.org/toc/1750-1172Abstract 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.Qian ZhuÐắc-Trung NguyễnTimothy SheilsGioconda AlyeaEric SidYanji XuJames DickensEwy A. MathéAnne PariserBMCarticleMedicineRENOrphanet Journal of Rare Diseases, Vol 16, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Qian Zhu Ðắc-Trung Nguyễn Timothy Sheils Gioconda Alyea Eric Sid Yanji Xu James Dickens Ewy A. Mathé Anne Pariser Scientific evidence based rare disease research discovery with research funding data in knowledge graph
description	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.
format	article
author	Qian Zhu Ðắc-Trung Nguyễn Timothy Sheils Gioconda Alyea Eric Sid Yanji Xu James Dickens Ewy A. Mathé Anne Pariser
author_facet	Qian Zhu Ðắc-Trung Nguyễn Timothy Sheils Gioconda Alyea Eric Sid Yanji Xu James Dickens Ewy A. Mathé Anne Pariser
author_sort	Qian Zhu
title	Scientific evidence based rare disease research discovery with research funding data in knowledge graph
title_short	Scientific evidence based rare disease research discovery with research funding data in knowledge graph
title_full	Scientific evidence based rare disease research discovery with research funding data in knowledge graph
title_fullStr	Scientific evidence based rare disease research discovery with research funding data in knowledge graph
title_full_unstemmed	Scientific evidence based rare disease research discovery with research funding data in knowledge graph
title_sort	scientific evidence based rare disease research discovery with research funding data in knowledge graph
publisher	BMC
publishDate	2021
url	https://doaj.org/article/6436e90bae0b4f9dbe8bc63d5bd9cd97
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Scientific evidence based rare disease research discovery with research funding data in knowledge graph

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