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Assembly and reasoning over semantic mappings at scale for biomedical data integration.

Charles Tapley Hoyt^1,2, Klas Karis², Benjamin M Gyori^2,3

¹Institute of Inorganic Chemistry, RWTH Aachen University, Aachen, NRW, 52074, Germany.

Bioinformatics (Oxford, England)

|September 29, 2025

Summary

SeMRA integrates biomedical concept identifiers from diverse sources, inferring missing links to enhance data interoperability. This tool creates a comprehensive database, connecting previously unlinked identifier spaces for better data integration.

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Area of Science:

Biomedical Informatics
Data Science
Bioinformatics

Background:

Numerous resources assign unique identifiers to biomedical concepts like genes, diseases, and cell types.
Overlapping identifiers across these resources create data interoperability challenges.
Integrating datasets requires mapping these disparate identifiers, but existing mappings are incomplete and fragmented.

Purpose of the Study:

To develop a scalable solution for integrating and mapping biomedical concept identifiers from multiple sources.
To address the data interoperability bottleneck caused by fragmented identifier mappings.
To create a comprehensive resource of biomedical concept mappings.

Main Methods:

Developed SeMRA, a software tool utilizing a graph data structure to integrate identifier mappings.
Employed graph algorithms to infer missing mappings and maintain provenance and confidence.
Implemented a customizable workflow accepting declarative specifications for source integration.

Main Results:

Created the SeMRA Raw Mappings Database, containing 43.4 million mappings from 127 sources.
The database covers identifiers from 445 ontologies and databases, connecting previously unlinked identifier spaces.
Demonstrated utility through benchmarks on specific use cases, such as disease and cell type resource integration.

Conclusions:

SeMRA effectively integrates fragmented biomedical identifier mappings into a unified graph structure.
The SeMRA Raw Mappings Database significantly enhances data interoperability for biomedical research.
The tool and database provide a valuable resource for connecting diverse biomedical knowledge bases.