A typical use case of ontologies is the calculation of similarity scores between items that are annotated with classes of the ontology. For example, in differential diagnostics and disease gene prioritization, the human phenotype ontology (HPO) is often used to compare a query phenotype profile against gold-standard phenotype profiles of diseases or genes. The latter have long been constructed as flat lists of ontology classes, which, as we show in this work, can be improved by exploiting existing structure and information in annotation datasets or full text disease descriptions. We derive a study-wise annotation model of diseases and genes and show that this can improve the performance of semantic similarity measures. Inferred weights of individual annotations are one reason for this improvement, but more importantly using the study-wise structure further boosts the results of the algorithms according to precision-recall analyses. We test the study-wise annotation model for diseases annotated with classes from the HPO and for genes annotated with gene ontology (GO) classes. We incorporate this annotation model into similarity algorithms and show how this leads to improved performance. This work adds weight to the need for enhancing simple list-based representations of disease or gene annotations. We show how study-wise annotations can be automatically derived from full text summaries of disease descriptions and from the annotation data provided by the GO Consortium and how semantic similarity measure can utilize this extended annotation model.
