Leader, Joseph B.

Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease

Dewey, F. E., Gusarova, V., Dunbar, R. L., O’Dushlaine, C., Schurmann, C., Gottesman, O., McCarthy, S., Van Hout, C. V., Bruse, S., Dansky, H. M., et al. (2017) · New England Journal of Medicine

Lipid metabolism and disordersLipoproteins and Cardiovascular Health

Cited by 885

Exome sequencing and characterization of 49,960 individuals in the UK Biobank

Van Hout, C. V., Tachmazidou, I., Backman, J. D., Hoffman, J. D., Liu, D., Pandey, A. K., Gonzaga-Jauregui, C., Khalid, S., Ye, B., Banerjee, N., et al. (2020) · Nature

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 561

Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease

Dewey, F. E., Gusarova, V., O’Dushlaine, C., Gottesman, O., Trejos, J., Hunt, C., Van Hout, C. V., Habegger, L., Buckler, D., Lai, K.-M. V., et al. (2016) · New England Journal of Medicine

Lipid metabolism and disordersLipoproteins and Cardiovascular Health

Cited by 497

Genetic identification of familial hypercholesterolemia within a single U.S. health care system

Abul-Husn, N. S., Manickam, K., Jones, L. K., Wright, E. A., Hartzel, D. N., Gonzaga-Jauregui, C., O’Dushlaine, C., Leader, J. B., Lester Kirchner, H., Lindbuchler, D. M., et al. (2016) · Science

Lipoproteins and Cardiovascular HealthGenomics and Rare Diseases

Cited by 425

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 314

Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation-related stroke

Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., et al. (2021) · Circulation

ECG Monitoring and AnalysisAtrial Fibrillation Management and Outcomes

Cited by 312

Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls

Platform, B. G., Flannick, J., DiscovEHR Collaboration, CHARGE, LuCamp, ProDiGY, GoT2D, ESP, SIGMA-T2D, T2D-GENES, et al. (2019) · Nature

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 304

Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease

Khera, A. V., Won, H.-H., Peloso, G. M., O’Dushlaine, C., Liu, D., Stitziel, N. O., Natarajan, P., Nomura, A., Emdin, C. A., Gupta, N., et al. (2017) · JAMA

Lipid metabolism and disordersLipoproteins and Cardiovascular Health

Cited by 198

Finding missed cases of familial hypercholesterolemia in health systems using machine learning

Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 123

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

Van Hout, C. V., Tachmazidou, I., Backman, J. D., Hoffman, J. X., Ye, B., Pandey, A. K., Gonzaga-Jauregui, C., Khalid, S., Liu, D., Banerjee, N., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory)

Genomics and Rare DiseasesGenetic Associations and Epidemiology

Cited by 110