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A Strategy for International Cooperation in the COVID-19 Pandemic Era: Focusing on National Scientific Funding Data.

ABSTRACT: The coronavirus crisis may lead to a deeper understanding of international collaborations for developing antivirals and vaccines that are essential to protect us from current and future health security threats. Beyond technical solutions, the government of South Korea needs to establish a timely strategic investment in coronavirus-related research and development (R&D) in order to enhance the capabilities for managing this new uncertainty in regard to the domestic health crisis. Thus, this study aims to provide useful information about the status of global coronavirus-related research from the South Korean government's perspective. National funded projects stemmed from leading nations such as the United States, countries of the European Union, and Japan between 2012 and 2018. Six research fields were derived by clustering analysis and an expert-based approach, and then matched to those of South Korea. The comparative analysis among them allowed for the identification of the nations' strengths and weaknesses, thereby laying the groundwork for strategic international research collaborations.

SUBMITTER: Lee D

PROVIDER: S-EPMC7551450 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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A Strategy for International Cooperation in the COVID-19 Pandemic Era: Focusing on National Scientific Funding Data.

Lee Doyeon D Heo Yoseob Y Kim Keunhwan K

Healthcare (Basel, Switzerland) 20200709 3

The coronavirus crisis may lead to a deeper understanding of international collaborations for developing antivirals and vaccines that are essential to protect us from current and future health security threats. Beyond technical solutions, the government of South Korea needs to establish a timely strategic investment in coronavirus-related research and development (R&D) in order to enhance the capabilities for managing this new uncertainty in regard to the domestic health crisis. Thus, this study ...[more]

PMID: 32659997

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Project description: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. Supplementary Information The online version contains supplementary material available at 10.1186/s13023-021-02120-9.

Dataset Information

A Strategy for International Cooperation in the COVID-19 Pandemic Era: Focusing on National Scientific Funding Data.

Publications

A Strategy for International Cooperation in the COVID-19 Pandemic Era: Focusing on National Scientific Funding Data.

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