MIM – started in 1966 and online (OMIM) for over a decade. It has been extremely difficult to use computationally in a large-scale fashion. Thehierarchical structure of OMIM does not reflect that two terms are more cloesly related than a third. In constructing the HPO, all descriptions used at least twice (~7000) were assigned to HPO. It now has about 9000 terms and annotations for 4813 diseases. They have a procedure which calculates phenotypic similarity by finding their most-specific common ancestor.
You can visualize the human phenome using HPO. They also have a query system that allows physicians to query what’s in the ontology. Also there is the Phenomizer, which is “next-generation diagnostics”. You can get a prioritized list of candidates.To validate the approach, they took 44 syndromes and went to literature to look at their frequency, then generate patients at random using the features of the disease. For each simulated patient, queries were generated using HPO terms. Ranks of the disease returned by the phenomizer were compared to the original diagnosis. Comparisons were performed with phenotypic noise. In an ideal situation, their approach has some advantage (when no noise and imprecision). When add noise or imprecision, the p-value stays ok but other measures drop. They also use the information to get disease-gene families.
HPO and PATO are talking to each other. HPO is being used as a link between cellular networks and HP. They also want you to annotate your data with HPO. If you’re interested, find out more about the HPO Consortium.
Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else’s. I’m happy to correct any errors you may spot – just let me know!