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• W2189288216 abstract "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 thesaurusbased approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching." @default.
• W2189288216 created "2016-06-24" @default.
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• W2189288216 date "2006-01-01" @default.
• W2189288216 modified "2023-09-26" @default.
• W2189288216 title "GeneRIF is a more comprehensive, current and computationally tractable source of gene-disease relationships than OMIM" @default.
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