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Items: 1 to 20 of 64

1.

Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank.

Pathak J, Kiefer RC, Bielinski SJ, Chute CG.

J Biomed Semantics. 2012 Dec 17;3(1):10. doi: 10.1186/2041-1480-3-10.

2.

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

Pathak J, Kiefer RC, Bielinski SJ, Chute CG.

AMIA Annu Symp Proc. 2012;2012:699-708. Epub 2012 Nov 3.

3.

Using semantic web technologies for cohort identification from electronic health records for clinical research.

Pathak J, Kiefer RC, Chute CG.

AMIA Jt Summits Transl Sci Proc. 2012;2012:10-9. Epub 2012 Mar 19.

4.

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Verma A, Leader JB, Verma SS, Frase A, Wallace J, Dudek S, Lavage DR, Van Hout CV, Dewey FE, Penn J, Lopez A, Overton JD, Carey DJ, Ledbetter DH, Kirchner HL, Ritchie MD, Pendergrass SA.

Pac Symp Biocomput. 2016;21:168-79.

5.

Semantically enabling a genome-wide association study database.

Beck T, Free RC, Thorisson GA, Brookes AJ.

J Biomed Semantics. 2012 Dec 17;3(1):9. doi: 10.1186/2041-1480-3-9.

6.

A Querying Method over RDF-ized Health Level Seven v2.5 Messages Using Life Science Knowledge Resources.

Kawazoe Y, Imai T, Ohe K.

JMIR Med Inform. 2016 Apr 5;4(2):e12. doi: 10.2196/medinform.5275.

7.

Using linked data for mining drug-drug interactions in electronic health records.

Pathak J, Kiefer RC, Chute CG.

Stud Health Technol Inform. 2013;192:682-6.

8.

Extracting research-quality phenotypes from electronic health records to support precision medicine.

Wei WQ, Denny JC.

Genome Med. 2015 Apr 30;7(1):41. doi: 10.1186/s13073-015-0166-y. eCollection 2015.

9.

Phenome-Wide Association Studies as a Tool to Advance Precision Medicine.

Denny JC, Bastarache L, Roden DM.

Annu Rev Genomics Hum Genet. 2016 Aug 31;17:353-73. doi: 10.1146/annurev-genom-090314-024956. Epub 2016 May 4. Review.

10.

Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases.

Verma A, Basile AO, Bradford Y, Kuivaniemi H, Tromp G, Carey D, Gerhard GS, Crowe JE Jr, Ritchie MD, Pendergrass SA.

PLoS One. 2016 Aug 10;11(8):e0160573. doi: 10.1371/journal.pone.0160573. eCollection 2016.

11.

The use of electronic health records for psychiatric phenotyping and genomics.

Smoller JW.

Am J Med Genet B Neuropsychiatr Genet. 2017 May 30. doi: 10.1002/ajmg.b.32548. [Epub ahead of print] Review.

PMID:
28557243
12.
13.

Validation and discovery of genotype-phenotype associations in chronic diseases using linked data.

Pathak J, Kiefer R, Freimuth R, Chute C.

Stud Health Technol Inform. 2012;180:549-53.

PMID:
22874251
14.

Mining Genotype-Phenotype Associations from Public Knowledge Sources via Semantic Web Querying.

Kiefer RC, Freimuth RR, Chute CG, Pathak J.

AMIA Jt Summits Transl Sci Proc. 2013 Mar 18;2013:118-22. eCollection 2013.

15.

Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies.

Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, Bradford Y, Chai HS, Bastarache L, Zuvich R, Peissig P, Carrell D, Ramirez AH, Pathak J, Wilke RA, Rasmussen L, Wang X, Pacheco JA, Kho AN, Hayes MG, Weston N, Matsumoto M, Kopp PA, Newton KM, Jarvik GP, Li R, Manolio TA, Kullo IJ, Chute CG, Chisholm RL, Larson EB, McCarty CA, Masys DR, Roden DM, de Andrade M.

Am J Hum Genet. 2011 Oct 7;89(4):529-42. doi: 10.1016/j.ajhg.2011.09.008.

16.

Cognitive IT-systems for big data analysis in medicine.

Isakova J.

Int J Risk Saf Med. 2015;27 Suppl 1:S108-9. doi: 10.3233/JRS-150711.

PMID:
26639685
17.

Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study.

Kho AN, Hayes MG, Rasmussen-Torvik L, Pacheco JA, Thompson WK, Armstrong LL, Denny JC, Peissig PL, Miller AW, Wei WQ, Bielinski SJ, Chute CG, Leibson CL, Jarvik GP, Crosslin DR, Carlson CS, Newton KM, Wolf WA, Chisholm RL, Lowe WL.

J Am Med Inform Assoc. 2012 Mar-Apr;19(2):212-8. doi: 10.1136/amiajnl-2011-000439. Epub 2011 Nov 19.

18.

EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group.

Frey LJ, Lenert L, Lopez-Campos G.

Yearb Med Inform. 2014 Aug 15;9:206-11. doi: 10.15265/IY-2014-0006.

19.

Integrating EMR-linked and in vivo functional genetic data to identify new genotype-phenotype associations.

Mosley JD, Van Driest SL, Weeke PE, Delaney JT, Wells QS, Bastarache L, Roden DM, Denny JC.

PLoS One. 2014 Jun 20;9(6):e100322. doi: 10.1371/journal.pone.0100322. eCollection 2014.

20.

Efficient genome-wide association in biobanks using topic modeling identifies multiple novel disease loci.

McCoy TH, Castro VM, Snapper LA, Hart KL, Perlis RH.

Mol Med. 2017 Aug 31;23. doi: 10.2119/molmed.2017.00100. [Epub ahead of print]

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