Progress toward a universal biomedical data translator
Karamarie Fecho, Anne Thessen, Sergio E. Baranzini, Chris Bizon, Jennifer Hadlock, Sui Huang, Ryan Roper, Noel Southall, Casey Ta, Paul B. Watkins, Mark D. Williams, Hao Xu, William E. Byrd, Vlado Dančík, Marc Duby, Michel Dumontier, Gustavo Glusman, Nomi L. Harris, Eugene W. Hinderer, Greg Hyde, Adam J. Johs, Andrew I. Su, Guangrong Qin, Qian Zhu
Abstract
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.