As industrial, governmental, and academic agencies place increasing emphasis on translational research, biomedical researchers are now faced with entirely new challenges in regards to both biomedical data integration and knowledge discovery. There is now both a strong need and a tremendous opportunity to apply translational bioinformatics to address the fundamental challenges in integrating the vast bodies of -omics and clinical data. Here we report on our preliminary work in utilizing SNOMED-CT as both a tool for translational data discovery, and a major component in a framework for the large-scale integration of gene expression microarray data and clinical laboratory data. Annotations from microarray experiments in NCBI GEO were mapped to SNOMED-CT terms using UMLS, and these mappings were joined to clinical laboratory data using ICD9CM to SNOMED-CT mappings within UMLS. We find that microarray experiments characterizing 211 distinct diseases can be mapped to clinical laboratory data measurements for 13,452 distinct patients. We maintain that this work represents critical first steps in providing a foundation for large-scale translational data integration, and underlines the important role that controlled clinical terminologies, such as SNOMEDCT, can play in addressing such problems.
