Motivation: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.
Methods: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to data mine gene-disease relationships from both the GeneRIF and OMIM databases. We utilized a comprehensive subset of UMLS structured as a directed acyclic graph (the Disease Ontology) to filter and interpret results from MMTx. The data mining methodology was validated against the Homayouni gene collection using recall and precision measurements.
Results: The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% (recall) and 98% (precision) using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching.

GeneRIF, OMIM, Disease Ontology, text data mining