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.
Dr. Jan Youtie (Co-PI) | Enterprise Innovation Institute and School of Public Policy (Georgia Tech)
Dr. Alan Porter (PI) | Search Technology
U.S. National Science Foundation, Award # 2029673
For more information
Contact: Alan Porter, Director of R&D, Search Technology, Inc, Norcross, GA 30092, USA. Tel: +1 404-384-6295