This node implements the Phenomizer algorithm for the PhenoDis database at Helmholtz Zentrum.
The Phenomizer method is an ontology-based similarity search algorithm. It compares a list of symptoms against a set of annotated diseases. The similarity measure of the algorithm makes use of a symptom ontology, i.e. a directed acyclic graph that represents an is-a hierarchy of the symptoms. The algorithm is described in detail in the Phenomizer paper by Koehler et al. (2009).
Phenomizer requires several input tables: Table 0 to 2 are directly extracted from PhenoDis. Table 3 contains the PhenoDis symptom_ids of the query symptoms. Note that the column names of the tables have to match the names specified in the Input Port section. For more information about the format of the input tables see example data from https://github.com/marie-sophie/mapra.
The output of Phenomizer is a list of diseases with similarity score and p value. The list is sorted according to p value (ascending) and score (descending). The score of a disease indicates the similarity of the query symptoms and the symptoms annotated for the disease. The p value of a disease helps to evaluate the significance of the score. Phenomizer uses the following categories to classify the p values:
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