Progress toward a universal biomedical data translator

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.

FIGURE 1

Overview of the Translator architecture. Note that while the high‐level architecture depicted in the figure is accurate, certain components may deviate slightly from the architecture in their approach to implementation. Abbreviations: SRI, Standards and Reference Implementation; TRAPI, Translator Reasoner Application Programming Interface. (Graphic prepared by Kelsey Urgo).

FIGURE 2

An example of a natural language question translated into a TRAPI directed query graph in JSON format. (a) the natural language question: what chemical entity(ies) treats chronic pain? (b) the natural language question represented as an object‐predicate‐subject “triple.” (c) the TRAPI query that was executed by Translator. TRAPI, Translator Reasoner Application Programming Interface. (Graphic prepared by Kelsey Urgo).

FIGURE 3

Screenshots demonstrating an example of Translator evidence and provenance in support of naltrexone hydrochloride as an answer to the query in Figure 2.

FIGURE 4

Schematic of three generalizable Translator workflows applied to support specific use‐case queries on (a) immune‐mediated inflammatory diseases, (b) Crohn's–Parkinson's disease relationship, and (c) drug‐induced liver injury. (Graphic prepared by Kelsey Urgo).