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When a specific GO term needs to be categorized, Categorizer searches for its parent terms that are assigned to a category and calculates semantic similarity scores with them.
The categorization accuracies of Categorizer and GO Slim are 81% and 70%, respectively.
We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington's disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.
Categorizer enables more accurate categorizations of genes than currently available methods.
Overall, these comparisons reveal that Categorizer provides more reliable categorization and enrichment results compared to the conventional GO analysis method.
Categorizer employs three steps to categorize genes.
These terms were mapped to high level gene ontology categories with CateGOrizer [ 60], using the "Aqua" categorization.
We report on the original integration of an automatic text categorization pipeline, so-called ToxiCat (Toxicogenomic Categorizer), that we developed to perform biomedical documents classification and prioritization in order to speed up the curation of the Comparative Toxicogenomics Database (CTD).
P g, c) = 1 if a gene g is categorized into a category c by Categorizer or the GO Slim-based method, respectively, and P g, c) = 0 if the gene g is not categorized into category c.
The "Aqua" classification in CateGOrizer [ 28] was used to categorize the GO terms into different GO categories.
Overall, these tests demonstrate that the category assignment of Categorizer is more accurate than a classical categorization with GO Slim terms and, thereby, underlines the importance of semantic similarity for this task.
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