Mining the human phenome using semantic web technologies: a case study for Type 2 Diabetes.

Jyotishman Pathak, Richard C. Kiefer, Suzette J. Bielinski, Christopher G. Chute

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

The ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form "biobanks" where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypothesis generation. In this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped with Type 2 Diabetes for discovering gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.

Original languageEnglish (US)
Pages (from-to)699-708
Number of pages10
JournalUnknown Journal
Volume2012
StatePublished - 2012

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint Dive into the research topics of 'Mining the human phenome using semantic web technologies: a case study for Type 2 Diabetes.'. Together they form a unique fingerprint.

  • Cite this