The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.
This project will create an organization of data called a knowledge network that will allow doctors, researchers, the pharmaceutical industry, and citizen scientists to much more effectively understand and explore biomedicine. It will connect vast amounts of data in a way that allows important new questions to be asked, helping to discover the root of a biological process, identify cures for diseases, recognize pharmaceuticals that could be relevant previously unexplored conditions, and much more. The platform will enable and support biomedical applications created by third parties. The adoption of those enabled tools has the potential to have significant societal impacts: reducing healthcare costs, health disparities and accelerating therapeutics, ultimately improving the quality of life for every American.
Healthcare costs Americans almost one-fifth of the entire US GDP. Health disparities, major public health issues, drug discovery complexity, and overall costs continue to grow dramatically. The mechanisms underlying human health are so complex that the human brain cannot integrate the ever-growing body of available knowledge relevant to treating patients or discovering therapies. This hampers the generation of new knowledge, specifically in the biomedical sciences and its implications for human health. The goal of this project, a biomedical open knowledge network (OKN), is to integrate billions of biomedical concepts into a knowledge engine that will enable doctors, drug developers, researchers, and citizen scientists to produce biologically meaningful answers to biomedical questions – rapidly and cheaply. This OKN will incorporate billions of factual relationships among biomedical concepts, allowing specialists to generalists to explore biomedicine in its whole might.
The team supported by this project is part of a group pioneering the paradigm of knowledge networks in biomedicine. The effort brings together partners with expertise in search tools (including Google), graph theory (from Lawrence Livermore National Labs), and collaboration with the National Center for Advancing Translational Sciences’ Biomedical Data Translator (part of the National Institutes of Health), as well as working with other academic nonprofit institutions (the Institute for Systems Biology, Indiana University, UC San Diego, and Stanford. The ambitious effort represents convergence research including expertise across all aspects of biomedicine and data science, integrating doctors, researchers, epistemologists, database specialists, computer scientists, and statisticians.
During Phase I of the Convergence Accelerator Program, the team developed and made available a fully functional biomedical knowledge network, the Scalable Precision Medicine Knowledge Engine (SPOKE, spoke.rbvi.ucsf.edu). This success supports the likelihood that the team will produce deliverables in this Phase II project that will have a positive impact on society.
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.
