1.
Coombes BJ, Landi I, Choi KW, et al. The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people. Psychological medicine. 2023;53(15):7368-7374. doi:10.1017/S0033291723000983.
1.
Khurshid S, Churchill TW, Diamant N, et al. Deep learned representations of the resting 12-lead electrocardiogram to predict V̇O2 at peak exercise. European journal of preventive cardiology. 2023. doi:10.1093/eurjpc/zwad321.
1.
Mayo KR, Basford MA, Carroll RJ, et al. The Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. Annual review of biomedical data science. 2023;6:443-464. doi:10.1146/annurev-biodatasci-122120-104825.
1.
Dashti HS, Redline S, Saxena R. Polygenic risk score identifies associations between sleep duration and diseases determined from an electronic medical record biobank. Sleep. 2019;42(3). doi:10.1093/sleep/zsy247.
1.
Song W, Huang H, Zhang C-Z, Bates DW, Wright A. Using whole genome scores to compare three clinical phenotyping methods in complex diseases. Sci Rep. 2018;8(1):11360. doi:10.1038/s41598-018-29634-w.
1.
Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141). doi:10.1098/rsif.2017.0387.
1.
Wolf SM, Amendola LM, Berg JS, et al. Navigating the research-clinical interface in genomic medicine: analysis from the CSER Consortium. Genet Med. 2018;20(5):545-553. doi:10.1038/gim.2017.137.
1.
Hess GP, Natarajan P, Faridi KF, Fievitz A, Valsdottir L, Yeh RW. Proprotein Convertase Subtilisin/Kexin Type 9 Inhibitor Therapy: Payer Approvals and Rejections, and Patient Characteristics for Successful Prescribing. Circulation. 2017;136(23):2210-2219. doi:10.1161/CIRCULATIONAHA.117.028430.
1.
Chen C-Y, Lee PH, Castro VM, et al. Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records. Transl Psychiatry. 2018;8(1):86. doi:10.1038/s41398-018-0133-7.
1.
Xia Z, Secor E, Chibnik LB, et al. Modeling disease severity in multiple sclerosis using electronic health records. PLoS One. 2013;8(11):e78927. doi:10.1371/journal.pone.0078927.