UMMS Affiliation

Program in Gene Function and Expression; Program in Molecular Medicine

Publication Date


Document Type



Computational Biology; *Databases, Genetic; *Genome, Human; Humans; *Software; *Unified Medical Language System


Genetics and Genomics


BACKGROUND: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.

RESULTS: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.

CONCLUSION: 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. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.

DOI of Published Version



BMC Genomics. 2009 Jul 7;10 Suppl 1:S6. Link to article on publisher's site

Journal/Book/Conference Title

BMC genomics

Related Resources

Link to Article in PubMed

PubMed ID




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