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

ABSTRACT:

Motivation

Hundreds of resources assign identifiers to biomedical concepts including genes, small molecules, biological processes, diseases, and cell types. Often, these resources overlap by assigning identifiers to the same or related concepts. This creates a data interoperability bottleneck, as integrating data sets and knowledge bases that use identifiers for the same concepts from different resources requires such identifiers to be mapped to each other. However, available mappings are incomplete and fragmented across individual resources, motivating their large-scale integration.

Results

We developed SeMRA, a software tool that integrates mappings from multiple sources into a graph data structure. Using graph algorithms, it infers missing mappings implied by available on

SUBMITTER: Hoyt CT 

PROVIDER: S-EPMC12629235 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature