Announcing the Biomedical Data Translator: Initial Public Release

The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open-source knowledge graph-based system-Translator-designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real-world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

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Publication Details

Journal
UNC Libraries
Published
2025-07-25
DOI
https://doi.org/10.17615/9as8-h722
Primary Topic
Biomedical Ethics and Regulation
Type
article
Field-Weighted Citation Impact
0.00
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article

Announcing the Biomedical Data Translator: Initial Public Release

Chris Mungall, David Koslicki, William E. Byrd, Robert Hubal et al.
UNC Libraries
Biomedical Ethics and Regulation
article

Announcing the Biomedical Data Translator: Initial Public Release

Chris Mungall, David Koslicki, William E. Byrd, Robert Hubal, Gustavo Glusman, Sergio E. Baranzini, Chunlei Wu, Sierra Moxon, Sui Huang, Lawrence Chung, Karthik Soman, Jared C. Roach, Mark D. Williams, Colleen Xu, Rosina O. Weber, Jana Marcette, Reese Rose, Guangrong Qin, Chris Bizon, Michel Dumontier, Andrew I. Su, Qi Wei, Jennifer Hadlock, Karamarie Fecho, Matthew Brush, Ewy A. Mathé, Casey Ta, Gaurav Vaidya, Chase Yakaboski, The Biomedical Data Translator Consortium, Eric W. Deutsch, Sharat Israni, Justin Reese, John D. Osborne, Aleksandra Foksinska, Gregory Hyde, Kaiwen He, Andrew B. Crouse, Stephen A. Ramsey, Kelyne Kenmogne, Abrar Mesbah, Carrie Pasfield
article en

Abstract

The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open-source knowledge graph-based system-Translator-designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real-world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 64%
Biomedical Ethics and Regulation
DOIOpen Access PDFOpenAlex
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