RAPID: Understanding the Transmission and Prevention of COVID-19 with Biomedical Knowledge Engineering

RAPID：利用生物医学知识工程了解 COVID-19 的传播和预防

• 批准号: 2027970
• 负责人: Fei Wang
• 金额: $ 20万
• 依托单位: Joan and Sanford I. Weill Medical College of Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027970&HistoricalAwards=false
• 关键词: RAPID; Understanding; Transmission; Prevention; COVID

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.

COVID-19是一场全球大流行，影响了数百个国家的数百万人。对新冠肺炎传播和预防认识的研究投入了大量精力。紧跟最新的研究对研究人员和实践者来说至关重要。然而，相关文献数量的快速增长使这一任务具有挑战性。本项目拟构建COVID-19专项知识图谱，方便获取COVID-19相关知识。知识图谱也将不断更新，利用自然语言处理技术从最新的生物医学文献中提取信息。集成知识图并使其作为web服务公开可用的软件管道，具有支持信息检索和问题回答的用户友好界面。将提供交互式可视化，供用户探索知识图并推导推理路径以获得答案。构建知识图谱的所有相关数据和源代码都将公开。本项目通过1)整合现有生物医学实体知识库，从生物学、临床和流行病学三个层面构建COVID-19特异性知识图谱；2)从最新的生物医学文献中提取新知识，不断增强构建的知识图谱。虽然知识图谱的目标是COVID-19，但开发的管道和技术也可以很容易地扩展到其他关键疾病。拟议研究的技术内容将影响公共卫生、生物医学信息学、计算机科学和流行病学。建议的研究结果将被纳入课堂。女性和代表性不足的研究人员，本科生和K-12学生，将积极参与本项目的研究工作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19.

• DOI: 10.1038/s41598-021-95431-7
• 发表时间: 2021-08-05
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Su C;Xu Z;Hoffman K;Goyal P;Safford MM;Lee J;Alvarez-Mulett S;Gomez-Escobar L;Price DR;Harrington JS;Torres LK;Martinez FJ;Campion TR Jr;Wang F;Schenck EJ
• 通讯作者: Schenck EJ