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Daniel M Jordan

Showing results (21-30 of 42) with videos related to

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Atherosclerosis|January 12, 2025
Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK BiobankKyle Gibson, Iain S Forrest, Ben O Petrazzini, et al.
NPJ Drug Discovery|October 6, 2025
Genetic evidence informs the direction of therapeutic modulation in drug developmentRobert Chen, Áine Duffy, Joshua K Park, et al.
Nature Communications|September 30, 2025
Development of a genetic priority score to predict drug side effects using human genetic evidenceÁine Duffy, Robert Chen, David Stein, et al.
Nature Communications|February 17, 2019
Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selectionArmin P Schoech, Daniel M Jordan, Po-Ru Loh, et al.
Nature Communications|May 11, 2023
A machine learning model identifies patients in need of autoimmune disease testing using electronic health recordsIain S Forrest, Ben O Petrazzini, Áine Duffy, et al.
American Journal of Human Genetics|February 12, 2011
Development and validation of a computational method for assessment of missense variants in hypertrophic cardiomyopathyDaniel M Jordan, Adam Kiezun, Samantha M Baxter, et al.
Nature Genetics|April 4, 2017
Estimating the selective effects of heterozygous protein-truncating variants from human exome dataChristopher A Cassa, Donate Weghorn, Daniel J Balick, et al.
JAMA|January 25, 2022
Population-Based Penetrance of Deleterious Clinical VariantsIain S Forrest, Kumardeep Chaudhary, Ha My T Vy, et al.
Science (New York, N.Y.)|August 28, 2025
Machine learning-based penetrance of genetic variantsIain S Forrest, Ha My T Vy, Ghislain Rocheleau, et al.
Nature Genetics|November 28, 2018
Reply to 'Selective effects of heterozygous protein-truncating variants'Christopher A Cassa, Donate Weghorn, Daniel J Balick, et al.
Pageof 5

Showing results (21-30 of 42) with videos related to

Sort By:
Pageof 5
Atherosclerosis|January 12, 2025
Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK BiobankKyle Gibson, Iain S Forrest, Ben O Petrazzini, et al.
NPJ Drug Discovery|October 6, 2025
Genetic evidence informs the direction of therapeutic modulation in drug developmentRobert Chen, Áine Duffy, Joshua K Park, et al.
Nature Communications|September 30, 2025
Development of a genetic priority score to predict drug side effects using human genetic evidenceÁine Duffy, Robert Chen, David Stein, et al.
Nature Communications|February 17, 2019
Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selectionArmin P Schoech, Daniel M Jordan, Po-Ru Loh, et al.
Nature Communications|May 11, 2023
A machine learning model identifies patients in need of autoimmune disease testing using electronic health recordsIain S Forrest, Ben O Petrazzini, Áine Duffy, et al.
American Journal of Human Genetics|February 12, 2011
Development and validation of a computational method for assessment of missense variants in hypertrophic cardiomyopathyDaniel M Jordan, Adam Kiezun, Samantha M Baxter, et al.
Nature Genetics|April 4, 2017
Estimating the selective effects of heterozygous protein-truncating variants from human exome dataChristopher A Cassa, Donate Weghorn, Daniel J Balick, et al.
JAMA|January 25, 2022
Population-Based Penetrance of Deleterious Clinical VariantsIain S Forrest, Kumardeep Chaudhary, Ha My T Vy, et al.
Science (New York, N.Y.)|August 28, 2025
Machine learning-based penetrance of genetic variantsIain S Forrest, Ha My T Vy, Ghislain Rocheleau, et al.
Nature Genetics|November 28, 2018
Reply to 'Selective effects of heterozygous protein-truncating variants'Christopher A Cassa, Donate Weghorn, Daniel J Balick, et al.
Pageof 5