How much can model organism phenotypes teach us about human disease? A study using ontologies and semantic machine learning

The use of model organisms such as the mouse, fruitfly and zebrafish has been key in driving our understanding of human disease and its underlying biology for arguably a century, mainly due to the availability of genetic approaches. Many thousands of phenotypic annotations are now available for the major experimental model organism. Different organisms offer different strengths and weaknesses. When combining the phenotypic annotations across multiple model organisms, the strengths and weaknesses of each model may be compensated and coverage of the human genome can be optimised. Work over the past decade has demonstrated the power of cross-species phenotypic comparisons, and cross-species phenotype ontologies such as uPheno and the PhenomeNET ontology have been developed for this purpose. We report further development of the pan-species phenotype ontology PhenomeNet-Extended (Pheno-e), in particular including phenotypes from Schizosaccharomyces and Drosophila. We apply ontology embeddings and unsupervised machine learning to measure the semantic similarity between phenotypes resulting from loss-of-function mutations in model organisms and their associated phenotypes. We demonstrate the different contributions of each species' phenotypic data to the identification of human gene-disease associations and investigate the physiological and anatomical properties through which each species contributes