TY - JOUR
T1 - Progress Toward a Universal Biomedical Data Translator
AU - Fecho, Karamarie
AU - Thessen, Anne T
AU - Baranzini, Sergio E
AU - Bizon, Chris
AU - Hadlock, Jennifer J
AU - Huang, Sui
AU - Roper, Ryan T
AU - Southall, Noel
AU - Ta, Casey
AU - Watkins, Paul B
AU - Williams, Mark D
AU - Xu, Hao
AU - Byrd, William
AU - Dančík, Vlado
AU - Duby, Marc P
AU - Dumontier, Michel
AU - Glusman, Gustavo
AU - Harris, Nomi L
AU - Hinderer, Eugene W
AU - Hyde, Greg
AU - Johs, Adam
AU - Su, Andrew
AU - Qin, Guangrong
AU - Zhu, Qian
AU - Biomedical Data Translator Consortium
N1 - This article is protected by copyright. All rights reserved.
PY - 2022/8
Y1 - 2022/8
N2 - 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 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.
AB - 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 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.
U2 - 10.1111/cts.13301
DO - 10.1111/cts.13301
M3 - (Systematic) Review article
C2 - 35611543
SN - 1752-8054
VL - 15
SP - 1838
EP - 1847
JO - Clinical and Translational Science
JF - Clinical and Translational Science
IS - 8
ER -