Genestrace: phenomic knowledge discovery via structured terminology.
The era of applied genomic medicine is quickly approaching accompanied by the increasing availability of detailed genetic information. Understanding the genetic etiology behind complex, multi-gene diseases remains an important challenge. In order to uncover the putative genetic etiology of complex diseases, we designed a method that explores the relationships between two major terminological and ontological resources: the Unified Medical Language System (UMLS) and the Gene Ontology (GO). The UMLS has a mainly clinical emphasis; Gene Ontology has become the standard for biological annotations of genes and gene products. Using statistical and semantic relationships within and between the two resources, we are able to infer relationships between disease concepts in the UMLS and gene products annotated using GO and its associated databases. We validated our inferences by comparing them to the known gene-disease relationships, as defined in the Online Mendelian Inheritance in Man's morbidmap (OMIM). The proof-of-concept methods presented here are unique in that they bypass the ambiguity of the direct extraction of gene or disease term from MEDLINE. Additionally, our methods provide direct links to clinically significant diseases through established terminologies or ontologies. The preliminary results presented here indicate the potential utility of exploiting the existing, manually curated relationships in biomedical resources as a tool for the discovery of potentially valuable new gene-disease relationships.
Cantor
Michael N
MN
Department of Medicine, Beth Israel Medical Center, New York, NY 10003, USA.
Sarkar
Indra Neil
IN
Bodenreider
Olivier
O
Lussier
Yves A
YA
D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D030561:Databases, Nucleic Acid; D015870:Gene Expression; D005826:Genetics, Medical; D023281:Genomics; D006801:Humans; D008953:Models, Anatomic; D008957:Models, Genetic