Progress toward a universal biomedical data translator

Journal Article · 24 May 2022 · Clinical and Translational Science

DOI: https://doi.org/10.1111/cts.13301 · OSTI ID:2470866

Fecho, Karamarie  ^[1]; Thessen, Anne E.  ^[2]; Baranzini, Sergio E. ^[3]; Bizon, Chris ^[1]; Hadlock, Jennifer J.  ^[4]; Huang, Sui ^[4]; Roper, Ryan T. ^[4]; Southall, Noel ^[5]; Ta, Casey ^[6]; Watkins, Paul B. ^[1]; Williams, Mark D.  ^[5]; Xu, Hao ^[1]; Byrd, William  ^[7]; Dančík, Vlado  ^[8]; Duby, Marc P. ^[8]; Dumontier, Michel ^[9]; Glusman, Gustavo  ^[4]; Harris, Nomi L.  ^[10]; Hinderer, Eugene W. ^[11]; Hyde, Greg ^[12] ; Johs, Adam  ^[13]; Su, Andrew I.  ^[14]; Qin, Guangrong ^[4]; Zhu, Qian ^[5]

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1. University of North Carolina at Chapel Hill, NC (United States)
2. University of Colorado Anschutz Medical Campus, Aurora, CO (United States)
3. University of California at San Francisco, CA (United States)
4. Institute for Systems Biology, Seattle, WA (United States)
5. National Institutes of Health, Rockville, MD (United States)
6. Columbia University, New York, NY (United States)
7. University of Alabama at Birmingham, AL (United States)
8. Broad Institute, Cambridge, MA (United States)
9. Maastricht University (The Netherlands)
10. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
11. Tufts Medical Center, Boston, MA (United States)
12. Dartmouth College, Hanover, NH (United States)
13. Drexel University, Philadelphia, PA (United States)
14. The Scripps Research Institute, La Jolla, CA (United States)

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE
• Contributing Organization: The Biomedical Data Translator Consortium
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2470866
• Journal Information: Clinical and Translational Science, Journal Name: Clinical and Translational Science Journal Issue: 8 Vol. 15; ISSN 1752-8054
• Publisher: WileyCopyright Statement
• Country of Publication: United States
• Language: English