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Validation and discovery of genotype-phenotype associations in chronic diseases using linked data
Abstract This study investigates federated SPARQL qThis 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.matics and IOS Press. All rights reserved.
Abstractsub This study investigates federated SPARQL qThis 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.matics and IOS Press. All rights reserved.
Bibtextype inproceedings  +
Doi 10.3233/978-1-61499-101-4-549  +
Has author Pathak J. + , Kiefer R. + , Freimuth R. + , Chute C. +
Has extra keyword 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 + , Database Management Systems + , Databases + , Genetic + , Epidemiological Monitoring + , Genetic Association Studies + , Genetic Predisposition to Disease + , Medical Record Linkage +
Has keyword Genotype-phenotype associations + , Linked data + , Semantic Wikis +
Isbn 9781614991007  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 549–553  +
Published in Studies in Health Technology and Informatics +
Pubmed 22874251  +
Title Validation and discovery of genotype-phenotype associations in chronic diseases using linked data +
Type conference paper  +
Volume 180  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 8 November 2014 08:38:14  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 08:38:14  +
DateThis property is a special property in this wiki. 2012  +
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Validation and discovery of genotype-phenotype associations in chronic diseases using linked data + Title
 

 

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