Currently, genomics data and data repositories in the public domain are expanding at an explosive pace. However, translational discoveries that could be made by mining such public resources are hampered if they lack standard terminologies to describe diagnoses, diseases, and experimental conditions. For discovery to proceed in the eras of e-science, researchers need tools to enable them to find all the data sets relevant to their area of study—spanning the biological scales from molecular studies to clinical medicine—and bridging the research modalities from high-throughput experiments to clinical trials and medical imaging. For example, a researcher studying the allelic variations in a gene would want to know all the pathways that are affected by that gene, the drugs whose effects could be modulated by the allelic variations in the gene, and any disease that could be caused by the gene, and the clinical trials that have studied drugs or diseases related to that gene. The knowledge needed to study such questions is available in public data sets; the challenge is finding that information. The key challenge is to annotate the various resource elements consistently to identify the biomedical concepts to which they relate. These resource elements range from experimental data sets in public repositories, to records of disease associations of gene products in mutation databases, to entries of clinical-trial descriptions. Creating ontology-based annotations from the metadata in biomedical resources and identifying diagnoses, pathological states, and experimental agents contained in those resources allows indexing of the resources, enabling end users to formulate flexible searches for biomedical data. We have been developed a system that is integrated with BioPortal known as Ontrez. Ontrez enables researchers to search for biomedical data (such as genomic data sets, medical images, clinical trials and published papers). Ontrez promotes translational research by enabling researchers to locate relevant biological data sets and to integrate them with clinical data to bridge the bench-to-bedside gap.
Ontrez processes the metadata-annotations of gene expression data sets, descriptions of radiology images, clinical-trial reports, as well as abstracts of Pubmed articles to annotate (or tag) them with terms from appropriate ontologies.
