Biomedical Data Translator

Authors

• MW Williams
• SA Abdollahi

Abstract

There is a vast amount of diverse data available to clinical, biomedical, and translational scientists that has the potential to improve human health and well-being. However, domain-specific language and non-standardized formatting often make this data difficult to process.
To address these challenges, the Biomedical Data Translator Consortium has developed a pilot Translator system. Translator takes existing biomedical data sets and decodes them into insights that can augment human reasoning and accelerate translational science research.
The main goals of this knowledge graph-based system are to:
- Build infrastructure to support and facilitate data-driven translational research on a large scale.
- Integrate as many datasets as possible and allow them to be cross-queried and reasoned over by translational researchers with minimal barriers, scalability, and accuracy.
- Leverage open data, including patient data and team science.
- Promote “serendipitous” discovery.
With this poster, we intend to introduce people to Translator, explain the architecture of the system, and walk them through an example use case to demonstrate how the system and its parts work together.

Scientific Focus Area: Computational Biology

This page was last updated on Tuesday, August 6, 2024