Interoperable Standards for Biomedical Databases

Abstract

Biomedical research generates data across thousands of databases -- genomic sequences in NCBI/ENA, protein structures in PDB, clinical records in EHRs, drug information in DrugBank, disease annotations in OMIM, pathway knowledge in Reactome -- yet integrating data across these silos remains a formidable challenge. Heterogeneous data formats, inconsistent identifier systems, incompatible ontologies, and varying access protocols create a fragmented landscape where researchers spend an estimated 60-80% of analysis time on data wrangling rather than scientific discovery. Interoperability standards -- FAIR principles, FHIR for clinical data, GA4GH standards for genomics, and biomedical ontologies -- aim to make data findable, accessible, interoperable, and reusable. We present the Biomedical Database Interoperability Framework (BDIF), evaluating five interoperability approaches -- semantic web and linked data, FHIR-based clinical data exchange, GA4GH genomic data standards, ontology-mediated integration, and knowledge graph federation -- across four integration scenarios (clinical-genomic linkage, multi-omics data fusion, cross-database drug-target assembly, and international cohort harmonisation). Our Interoperability Effectiveness Score (IES) measures data linkage completeness, query performance, semantic accuracy, adoption feasibility, and scalability. Knowledge graph federation achieves the highest IES (0.922) through schema-agnostic entity resolution and federated SPARQL queries, while FHIR-based exchange achieves the highest adoption feasibility (0.960) through alignment with existing healthcare IT infrastructure.