RAPID: Understanding the Transmission and Prevention of COVID-19 with Biomedical Knowledge Engineering
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$199,966Funder
National Science Foundation (NSF)Principal Investigator
Fei WangResearch Location
United States of AmericaLead Research Institution
Joan and Sanford I. Weill Medical College of Cornell UniversityResearch Priority Alignment
N/A
Research Category
13
Research Subcategory
N/A
Special Interest Tags
Data Management and Data Sharing
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
COVID-19 has been a global pandemic affecting millions of people across hundreds of countries. Great
efforts have been devoted to the research on the understanding of the transmission and prevention of
COVID-19. Staying up-to-date with the latest research is crucial to the researchers and practitioners. However, the rapid growing of the number of the relevant literature makes this a challenging task. This project proposes to build a COVID 19 specific knowledge graph to facilitate the acquisition of COVID-19 related knowledge. The knowledge graph will also be continuously updated with the information extracted from latest biomedical literature with natural language processing techniques. A software pipeline that integrates and makes the knowledge graph publicly available as a web service with user friendly interface supporting information retrieval and question answering. Interactive visualization will be provided for the users to explore the knowledge graph and derive the inference paths to obtain the answers. All associated data and source codes for constructing the knowledge graph will be made publicly available.
This project constructs a COVID-19 specific knowledge graph by 1) integrating the existing knowledge
bases on biomedical entities from biological, clinical and epidemiological scales; 2) extracting new
knowledge from latest biomedical literature and enhancing the constructed knowledge graph
continuously. Although the knowledge graph targets COVID-19, the developed pipeline and
techniques can be easily extended to other critical diseases as well. The technical content of the proposed research will impact public health, biomedical informatics, computer science and epidemiology. The results of the proposed research will be incorporated into the classes. Females and underrepresented researchers, undergraduates and K-12 students, will be actively engaged in the research effort of this project.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
efforts have been devoted to the research on the understanding of the transmission and prevention of
COVID-19. Staying up-to-date with the latest research is crucial to the researchers and practitioners. However, the rapid growing of the number of the relevant literature makes this a challenging task. This project proposes to build a COVID 19 specific knowledge graph to facilitate the acquisition of COVID-19 related knowledge. The knowledge graph will also be continuously updated with the information extracted from latest biomedical literature with natural language processing techniques. A software pipeline that integrates and makes the knowledge graph publicly available as a web service with user friendly interface supporting information retrieval and question answering. Interactive visualization will be provided for the users to explore the knowledge graph and derive the inference paths to obtain the answers. All associated data and source codes for constructing the knowledge graph will be made publicly available.
This project constructs a COVID-19 specific knowledge graph by 1) integrating the existing knowledge
bases on biomedical entities from biological, clinical and epidemiological scales; 2) extracting new
knowledge from latest biomedical literature and enhancing the constructed knowledge graph
continuously. Although the knowledge graph targets COVID-19, the developed pipeline and
techniques can be easily extended to other critical diseases as well. The technical content of the proposed research will impact public health, biomedical informatics, computer science and epidemiology. The results of the proposed research will be incorporated into the classes. Females and underrepresented researchers, undergraduates and K-12 students, will be actively engaged in the research effort of this project.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.