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David S Carrell

Showing results (51-60 of 82) with videos related to

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BMC Medical Informatics and Decision Making|January 29, 2022
Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitionsJingzhi Yu, Jennifer A Pacheco, Anika S Ghosh, et al.
Journal of Biomedical Informatics|September 23, 2019
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE networkNing Shang, Cong Liu, Luke V Rasmussen, et al.
Journal of the American Medical Informatics Association : JAMIA|December 28, 2018
Enrichment sampling for a multi-site patient survey using electronic health records and census dataNathaniel D Mercaldo, Kyle B Brothers, David S Carrell, et al.
Science (New York, N.Y.)|February 26, 2016
The phenotypic legacy of admixture between modern humans and NeandertalsCorinne N Simonti, Benjamin Vernot, Lisa Bastarache, et al.
BMC Medical Genomics|January 7, 2021
Association between triglycerides, known risk SNVs and conserved rare variation in SLC25A40 in a multi-ancestry cohortElisabeth A Rosenthal, David R Crosslin, Adam S Gordon, et al.
Genetic Epidemiology|September 23, 2020
Loci identified by a genome-wide association study of carotid artery stenosis in the eMERGE networkMelody R Palmer, Daniel S Kim, David R Crosslin, et al.
Human Genetics|November 17, 2016
Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohortsRishika De, Shefali S Verma, Emily Holzinger, et al.
Genetic Epidemiology|May 19, 2015
Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE NetworkMolly A Hall, Shefali S Verma, John Wallace, et al.
Journal of the American Medical Informatics Association : JAMIA|December 7, 2022
Characterizing variability of electronic health record-driven phenotype definitionsPascal S Brandt, Abel Kho, Yuan Luo, et al.
Plos One|December 19, 2013
Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS dataJonathan D Mosley, Sara L Van Driest, Emma K Larkin, et al.
Pageof 9

Showing results (51-60 of 82) with videos related to

Sort By:
Pageof 9
BMC Medical Informatics and Decision Making|January 29, 2022
Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitionsJingzhi Yu, Jennifer A Pacheco, Anika S Ghosh, et al.
Journal of Biomedical Informatics|September 23, 2019
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE networkNing Shang, Cong Liu, Luke V Rasmussen, et al.
Journal of the American Medical Informatics Association : JAMIA|December 28, 2018
Enrichment sampling for a multi-site patient survey using electronic health records and census dataNathaniel D Mercaldo, Kyle B Brothers, David S Carrell, et al.
Science (New York, N.Y.)|February 26, 2016
The phenotypic legacy of admixture between modern humans and NeandertalsCorinne N Simonti, Benjamin Vernot, Lisa Bastarache, et al.
BMC Medical Genomics|January 7, 2021
Association between triglycerides, known risk SNVs and conserved rare variation in SLC25A40 in a multi-ancestry cohortElisabeth A Rosenthal, David R Crosslin, Adam S Gordon, et al.
Genetic Epidemiology|September 23, 2020
Loci identified by a genome-wide association study of carotid artery stenosis in the eMERGE networkMelody R Palmer, Daniel S Kim, David R Crosslin, et al.
Human Genetics|November 17, 2016
Identifying gene-gene interactions that are highly associated with four quantitative lipid traits across multiple cohortsRishika De, Shefali S Verma, Emily Holzinger, et al.
Genetic Epidemiology|May 19, 2015
Biology-Driven Gene-Gene Interaction Analysis of Age-Related Cataract in the eMERGE NetworkMolly A Hall, Shefali S Verma, John Wallace, et al.
Journal of the American Medical Informatics Association : JAMIA|December 7, 2022
Characterizing variability of electronic health record-driven phenotype definitionsPascal S Brandt, Abel Kho, Yuan Luo, et al.
Plos One|December 19, 2013
Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS dataJonathan D Mosley, Sara L Van Driest, Emma K Larkin, et al.
Pageof 9