Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Their computational analysis has been greatly facilitated by the introduction of phenotype ontologies developed to capture and compare phenotypes within the context of a single species. We have recently developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the cross-species phenotype network PhenomeNet. PhenomeNet relies on the descriptions of diseases and disorder with clinical signs to identifying causative genes for human diseases based on experimental data from animal organisms. We describe our integration of the Orphanet clinical signs for rare and orphan disease with PhenomeNet, and demonstrate that our approach can identify candidate genes through the systematic comparison of experimentally derived phenotypes in mice with clinical signs associated with Orphanet disorder (0.798 area under ROC curve).