| Resumo: |
Effective translational research requires automated analysis of large datasets collected by multiple researchers working at multiple locations. Reliable, machine interpretation of-—and reasoning with—-large datasets assembled at different times and places by different researchers requires standard representations of data. These representations are controlled, structured vocabularies also known as ontologies. By far, the most successful ontology is the Gene Ontology (GO), used by bioinformatics researchers to annotate genomics data. However, to address the phenotype side of translational research will require annotation of electronic medical record data and clinical research data with a clinical-phenotype ontology analogous to GO. One leading candidate for this ontology is SNOMED-CT (SNCT). However, GO and SNCT are incompatible representations. GO is based on an upper level ontology called Basic Formal Ontology (BFO). In this work, we aligned the upper level of SNCT with BFO to enhance its suitability for translational research. Most (14/19 or 74%) of the top-level concepts of SNCT can be fitted into the framework of BFO, but only after significant reorganization. An important concept that does not align is Clinical Finding, which is intended to comprehend diseases and signs and symptoms of disease. However, a finding of disease (epistemology) is not the same thing as a disease (ontology). This discrepancy between SNCT and BFO is important to consider further. Another key result is that children of the top-level concepts do not necessarily follow their parents into BFO, and thus one must align each SNCT concept independently. Future work is to align the next level of SNCT (345 concepts) with BFO.
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