Validation and discovery of genotype-phenotype associations in chronic diseases using linked data
|Validation and discovery of genotype-phenotype associations in chronic diseases using linked data|
|Author(s)||Pathak J., Kiefer R., Freimuth R., Chute C.|
|Published in||Studies in Health Technology and Informatics|
|Keyword(s)||Genotype-phenotype associations, Linked Data, Semantic Wikis (Extra: article, chronic disease, classification, data base, data mining, epidemiological monitoring, genetic association, genetic database, genetic predisposition, genetics, genotype, medical record, methodology, validation study, Chronic Disease, Data Mining, Database Management Systems, Databases, Genetic, Epidemiological Monitoring, Genetic Association Studies, Genetic Predisposition to Disease, Genotype, Medical Record Linkage)|
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Validation and discovery of genotype-phenotype associations in chronic diseases using linked data is a 2012 conference paper written in English by Pathak J., Kiefer R., Freimuth R., Chute C. and published in Studies in Health Technology and Informatics.
This study investigates federated SPARQL queries over Linked Open Data (LOD) in the Semantic Web to validate existing, and potentially discover new genotype-phenotype associations from public datasets. In particular, we report our preliminary findings for identifying such associations for commonly occurring chronic diseases using the Online Mendelian Inheritance in Man (OMIM) and Database for SNPs (dbSNP) within the LOD knowledgebase and compare them with Gene Wiki for coverage and completeness. Our results indicate that Semantic Web technologies can play an important role for in-silico identification of novel disease-gene-SNP associations, although additional verification is required before such information can be applied and used effectively. © 2012 European Federation for Medical Informatics and IOS Press. All rights reserved.
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