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Rare disease-based scientific annotation knowledge graph

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 appr...

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Autores principales: Zhu, Qian, Qu, Chunxu, Liu, Ruizheng, Vatas, Gunjan, Clough, Andrew, Nguyễn, Ðắc-Trung, Sid, Eric, Mathé, Ewy, Xu, Yanji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403737/
https://www.ncbi.nlm.nih.gov/pubmed/36034595
http://dx.doi.org/10.3389/frai.2022.932665
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author Zhu, Qian
Qu, Chunxu
Liu, Ruizheng
Vatas, Gunjan
Clough, Andrew
Nguyễn, Ðắc-Trung
Sid, Eric
Mathé, Ewy
Xu, Yanji
author_facet Zhu, Qian
Qu, Chunxu
Liu, Ruizheng
Vatas, Gunjan
Clough, Andrew
Nguyễn, Ðắc-Trung
Sid, Eric
Mathé, Ewy
Xu, Yanji
author_sort Zhu, Qian
collection PubMed
description 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.
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spelling pubmed-94037372022-08-26 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 Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2022-08-11 /pmc/articles/PMC9403737/ /pubmed/36034595 http://dx.doi.org/10.3389/frai.2022.932665 Text en Copyright © 2022 Zhu, Qu, Liu, Vatas, Clough, Nguyễn, Sid, Mathé and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Zhu, Qian
Qu, Chunxu
Liu, Ruizheng
Vatas, Gunjan
Clough, Andrew
Nguyễn, Ðắc-Trung
Sid, Eric
Mathé, Ewy
Xu, Yanji
Rare disease-based scientific annotation knowledge graph
title Rare disease-based scientific annotation knowledge graph
title_full Rare disease-based scientific annotation knowledge graph
title_fullStr Rare disease-based scientific annotation knowledge graph
title_full_unstemmed Rare disease-based scientific annotation knowledge graph
title_short Rare disease-based scientific annotation knowledge graph
title_sort rare disease-based scientific annotation knowledge graph
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403737/
https://www.ncbi.nlm.nih.gov/pubmed/36034595
http://dx.doi.org/10.3389/frai.2022.932665
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