Despite considerable progress in understanding the molecular origins of 
hereditary human diseases, the molecular basis of several thousand genetic 
diseases still remains unknown. High-throughput phenotype studies are underway 
to systematically assess the phenotype outcome of targeted mutations in model 
organisms. Thus, comparing the similarity between experimentally identified 
phenotypes and the phenotypes associated with human diseases can be used to 
suggest causal genes underlying a disease. In this manuscript, we present a 
method for disease gene prioritization based on comparing phenotypes of mouse 
models with those of human diseases. For this purpose, either human disease 
phenotypes are "translated" into a mouse-based representation (using the 
Mammalian Phenotype Ontology), or mouse phenotypes are "translated" into a 
human-based representation (using the Human Phenotype Ontology). We apply a 
measure of semantic similarity and rank experimentally identified phenotypes in 
mice with respect to their phenotypic similarity to human diseases. Our method 
is evaluated on manually curated and experimentally verified gene-disease 
associations for human and for mouse. We evaluate our approach using a Receiver 
Operating Characteristic (ROC) analysis and obtain an area under the ROC curve 
of up to . Furthermore, we are able to confirm previous results that the Vax1 
gene is involved in Septo-Optic Dysplasia and suggest Gdf6 and Marcks as further 
potential candidates. Our method significantly outperforms previous 
phenotype-based approaches of prioritizing gene-disease associations. To enable 
the adaption of our method to the analysis of other phenotype data, our software 
and prioritization results are freely available under a BSD licence at 
http://code.google.com/p/phenomeblast/wiki/CAMP. Furthermore, our method has 
been integrated in PhenomeNET and the results can be explored using the 
PhenomeBrowser at http://phenomebrowser.net.