Assessing human disease phenotype similarity based on ontology
Phenotypical similarity between diseases is an important factor in biomedical research since it has been used in many disease-related studies. More specifically, this information improves prediction performance of methods proposed for identifying disease-associated cellular components. In addition, this is also useful to figure out relationship among diseases to explain disease co-morbidity phenomenon. However, calculating and assessing the similarity among disease phenotypes is still an open question. Indeed, most studies have utilized an out-of-date disease phenotype similarity database constructed from OMIM records, which is also considered as a “gold standard” one, while only a few of studies have calculated this similarity based on human phenotype ontology. Considering that many methods have been defined to measure the similarity between two ontology-annotated entities, assessing which one is the best is crucial. In this study, we calculate the similarity for a set of 9,221,365 pairs of disease phenotypes using a number of ontology-based semantic similarity measures and assess them by correlating them with the “gold standard” one. Simulation results showed that, for both types of correlation (i.e., Pearson and Spearman correlations), the largest correlation coefficient is 0.58 for both pairwise between-entity similarity methods. Interestingly, these results are consistent with previous studies which assess similarity between genes based on gene ontology.