Metadata harmonization from biological datasets with language models.

ABSTRACT:

Motivation

Integrating biomedical datasets is hindered by inconsistent metadata, where the same concept may be represented in many ways (e.g. "Ca.," "Carcinoma," "tumor" for "Neoplasm"). Metadata harmonization automatically converts these researcher-specific terms into standard vocabulary terms to enable downstream integration. Current solutions, such as Common Data Elements and laboratory information management systems, either require navigating thousands of subtly different terms or disrupt researcher workflows, resulting in siloed datasets. This fragmentation forces researchers to spend over 40% of curation time on manual standardization.

Results

We present a language model-based harmonization solution that automatically maps researcher-specific metadata to standard terms

SUBMITTER: Verbitsky A 

PROVIDER: S-EPMC12716857 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature