RAPID: Exploring Causes and Cures for COVID-19 through Improved Access to Biomedical Research

RAPID：通过改善生物医学研究的可及性探索 COVID-19 的原因和治疗方法

• 批准号: 2029673
• 负责人: Alan Porter
• 金额: $ 18.62万
• 依托单位: Search Technology Inc
• 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=2029673&HistoricalAwards=false
• 关键词: RAPID; Exploring; Causes; Cures; COVID; RAPID; Exploring; Causes; Cures; COVID

Resolving the COVID-19 pandemic, and future crises, demands rapid and comprehensive access to pertinent research knowledge. This project promotes the progress of science to advance national health and welfare by enhancing the nation’s capability to understand the related global research literature and to identify findings in disparate fields that are relevant for combating the virus. This body of relevant literature comprises tens of thousands of biomedical articles and is growing explosively each week. This project will provide means to access vital findings and to develop tools to discover connections across disciplines and among causes, biomarkers, conditions, and treatments for COVID-19. The project will provide bi-weekly summaries of key findings in the biomedical literature pertaining to COVID-19 on an open website. The information will be arranged by topic, country, and organization, so that it can be easily accessed by researchers and clinicians. The project will also uncover connections across research domains that could identify novel treatments. Both approaches strive to connect disparate research findings and innovative developments. The first approach profiles the directly relevant research literature to make findings more accessible and allow researchers to discover complementary knowledge. Literature Based Discovery methods, using machine learning and related methods, help bridge distinct research findings. The project will make available a spectrum of research knowledge to help develop prevention and treatment for corona viruses. Literature Based Discovery methods serve to extract key components of a target domain and then explore other distinct domains for potential causes, vital biomechanisms, and treatments that could be repurposed. This approach seeks to discover previously unrecognized commonalities in research on other viruses that affect key physiological systems susceptible to the target viruses (for example respiratory and immune systems). Such commonalities, such as in ways that different viruses affect particular biosystems, may open doors to recognition of co-morbidities or development of novel treatments. The project will advance understanding of text mining and scientific discovery. The work will employ human judges to classify a moderate number of abstract records on multiple dimensions (such as virus type, physiological mechanisms addressed, causes, drug types, and other treatment modalities), then use software to auto-classify the 30,000 and growing COVID-19-related research articles. Clues emerging from such classification could open windows into other biomedical research and clinical studies to seek novel approaches. By making these data immediately and publicly available, the project will serve the community of those researching and practicing in the area and accelerate scientific discovery around the coronavirus.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.

解决COVID-19的大流行以及未来的犯罪，需要快速而全面地获得相关研究知识。该项目通过增强美国了解全球研究文献的能力并确定与梳理病毒相关的不同领域的发现，从而促进了科学的进步，以增强国家的能力来促进国家卫生和福利。相关文献的这一体系包括成千上万的生物医学文章，每周都在爆炸性地增长。该项目将每两周提供有关与Covid-19有关的生物医学文献中关键发现的摘要。该信息将按主题，国家和组织安排，以便研究人员和临床医生可以轻松地访问它。该项目还将发现可以识别新疗法的研究领域之间的联系。两种方法都努力将不同的研究发现和创新的发展联系起来。第一种方法介绍了直接相关的研究文献，以使发现更容易访问，并允许研究人员发现完整的知识。基于文献的发现方法，使用机器学习和相关方法，有助于弥合不同的研究结果。该项目将提供一系列研究知识，以帮助预防电晕病毒的开发和治疗。基于文献的发现方法用于提取目标域的关键组成部分，然后探索其他不同的域，以解决潜在的原因，重要的生物力学和可以重新使用的治疗方法。这种方法试图发现对影响关键生理系统（例如呼吸道和免疫系统）的关键生理系统的其他病毒的研究。这样的共同点，例如以不同的病毒影响特定生物系统的方式，可能会为认识合并症或新疗法发展的识别打开大门。该项目将提高对文本挖掘和科学发现的理解。这项工作将采用人类法官对多个维度（例如病毒类型，涉及的物理机制，原因，药物类型和其他治疗方式）进行分类，然后使用软件自动分类30,000，并增长了与COVID-19的与COVID相关的研究文章。从这种分类中提出的线索可能会将窗户打开其他生物医学研究和临床研究，以寻求新方法。通过立即并公开提供这些数据，该项目将为那些在该地区进行研究和练习的人提供社区，并加速围绕冠状病毒的科学发现。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估标准来评估通过评估而被认为是珍贵的。