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Items: 1 to 20 of 148

1.

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.

2.

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions.

Li H, Achour I, Bastarache L, Berghout J, Gardeux V, Li J, Lee Y, Pesce L, Yang X, Ramos KS, Foster I, Denny JC, Moore JH, Lussier YA.

NPJ Genom Med. 2016;1. pii: 16006. Epub 2016 Apr 27.

PMID:
27482468
3.

Approaches to uncovering cancer diagnostic and prognostic molecular signatures.

Hong S, Huang Y, Cao Y, Chen X, Han JD.

Mol Cell Oncol. 2014 Oct 29;1(2):e957981. doi: 10.4161/23723548.2014.957981. eCollection 2014 Apr-Jun.

4.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.

Glicksberg BS, Li L, Badgeley MA, Shameer K, Kosoy R, Beckmann ND, Pho N, Hakenberg J, Ma M, Ayers KL, Hoffman GE, Dan Li S, Schadt EE, Patel CJ, Chen R, Dudley JT.

Bioinformatics. 2016 Jun 15;32(12):i101-i110. doi: 10.1093/bioinformatics/btw282.

5.

DermO; an ontology for the description of dermatologic disease.

Fisher HM, Hoehndorf R, Bazelato BS, Dadras SS, King LE Jr, Gkoutos GV, Sundberg JP, Schofield PN.

J Biomed Semantics. 2016 Jun 13;7:38. doi: 10.1186/s13326-016-0085-x.

6.

Extracting a stroke phenotype risk factor from Veteran Health Administration clinical reports: an information content analysis.

Mowery DL, Chapman BE, Conway M, South BR, Madden E, Keyhani S, Chapman WW.

J Biomed Semantics. 2016 May 10;7:26. doi: 10.1186/s13326-016-0065-1. eCollection 2016.

7.

Targeting the untargeted in molecular phenomics with structurally-selective ion mobility-mass spectrometry.

May JC, Gant-Branum RL, McLean JA.

Curr Opin Biotechnol. 2016 Jun;39:192-7. doi: 10.1016/j.copbio.2016.04.013. Epub 2016 Apr 29. Review.

PMID:
27132126
8.

Identifying genetically driven clinical phenotypes using linear mixed models.

Mosley JD, Witte JS, Larkin EK, Bastarache L, Shaffer CM, Karnes JH, Stein CM, Phillips E, Hebbring SJ, Brilliant MH, Mayer J, Ye Z, Roden DM, Denny JC.

Nat Commun. 2016 Apr 25;7:11433. doi: 10.1038/ncomms11433.

9.

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.

Leader JB, Pendergrass SA, Verma A, Carey DJ, Hartzel DN, Ritchie MD, Kirchner HL.

AMIA Annu Symp Proc. 2015 Nov 5;2015:824-32. eCollection 2015.

10.

Mining and Visualizing Family History Associations in the Electronic Health Record: A Case Study for Pediatric Asthma.

Chen ES, Melton GB, Wasserman RC, Rosenau PT, Howard DB, Sarkar IN.

AMIA Annu Symp Proc. 2015 Nov 5;2015:396-405. eCollection 2015.

11.

The phenotypic legacy of admixture between modern humans and Neandertals.

Simonti CN, Vernot B, Bastarache L, Bottinger E, Carrell DS, Chisholm RL, Crosslin DR, Hebbring SJ, Jarvik GP, Kullo IJ, Li R, Pathak J, Ritchie MD, Roden DM, Verma SS, Tromp G, Prato JD, Bush WS, Akey JM, Denny JC, Capra JA.

Science. 2016 Feb 12;351(6274):737-41. doi: 10.1126/science.aad2149.

12.

Joint mouse-human phenome-wide association to test gene function and disease risk.

Wang X, Pandey AK, Mulligan MK, Williams EG, Mozhui K, Li Z, Jovaisaite V, Quarles LD, Xiao Z, Huang J, Capra JA, Chen Z, Taylor WL, Bastarache L, Niu X, Pollard KS, Ciobanu DC, Reznik AO, Tishkov AV, Zhulin IB, Peng J, Nelson SF, Denny JC, Auwerx J, Lu L, Williams RW.

Nat Commun. 2016 Feb 2;7:10464. doi: 10.1038/ncomms10464.

13.

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.

14.

Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.

Chen R, Sun J, Dittus RS, Fabbri D, Kirby J, Laffer CL, McNaughton CD, Malin B.

IEEE J Biomed Health Inform. 2016 Jan 4. [Epub ahead of print]

PMID:
26742152
15.

A Phenome-Wide Association Study Identifies a Novel Asthma Risk Locus Near TERC.

Claar DD, Larkin EK, Bastarache L, Blackwell TS, Loyd JE, Hartert TV, Denny JC, Kropski JA.

Am J Respir Crit Care Med. 2016 Jan 1;193(1):98-100. doi: 10.1164/rccm.201507-1267LE. No abstract available.

PMID:
26720789
16.

Integrating electronic health record genotype and phenotype datasets to transform patient care.

Roden DM, Denny JC.

Clin Pharmacol Ther. 2016 Mar;99(3):298-305. doi: 10.1002/cpt.321. Epub 2016 Jan 26.

PMID:
26667791
17.

Systems Genetic Validation of the SNP-Metabolite Association in Rice Via Metabolite-Pathway-Based Phenome-Wide Association Scans.

Lu Y, Liu Y, Niu X, Yang Q, Hu X, Zhang HY, Xia J.

Front Plant Sci. 2015 Nov 27;6:1027. doi: 10.3389/fpls.2015.01027. eCollection 2015.

18.

Towards a phenome-wide catalog of human clinical traits impacted by genetic ancestry.

Dumitrescu L, Restrepo NA, Goodloe R, Boston J, Farber-Eger E, Pendergrass SA, Bush WS, Crawford DC.

BioData Min. 2015 Nov 11;8:35. doi: 10.1186/s13040-015-0068-y. eCollection 2015.

19.

Identification of type 2 diabetes subgroups through topological analysis of patient similarity.

Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R, Chen R, Bottinger EP, Dudley JT.

Sci Transl Med. 2015 Oct 28;7(311):311ra174. doi: 10.1126/scitranslmed.aaa9364.

20.

Proceedings of the 14th Annual UT-KBRIN Bioinformatics Summit 2015.

[No authors listed]

BMC Bioinformatics. 2015;16 Suppl 15:I1-P21. Epub 2015 Oct 23. No abstract available.

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