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Genetic Epidemiology|February 11, 2020
PANDA: Prioritization of autism-genes using network-based deep-learning approachYu Zhang, Yuanzhu Chen, Ting HuGenetic Epidemiology|February 12, 2020
Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analysesMengqi Zhang, Sahar Gelfman, Janice McCarthy, et al.Genetic Epidemiology|February 13, 2020
Power calculation for the general two-sample Mendelian randomization analysisLu Deng, Han Zhang, Kai YuGenetic Epidemiology|March 3, 2020
Transcriptome-wide association study of breast cancer risk by estrogen-receptor statusHelian Feng, Alexander Gusev, Bogdan Pasaniuc, et al.Genetic Epidemiology|March 27, 2015
Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear modelsYifan Wang, Aiyi Liu, James L Mills, et al.Genetic Epidemiology|January 11, 2014
How to include chromosome X in your genome-wide association studyInke R König, Christina Loley, Jeanette Erdmann, et al.Genetic Epidemiology|August 3, 2020
Chances and challenges of machine learning-based disease classification in genetic association studies illustrated on age-related macular degenerationFelix Guenther, Caroline Brandl, Thomas W Winkler, et al.Genetic Epidemiology|January 1, 1988
A nationwide evaluation of multiple congenital abnormalities in HungaryA Czeizel, M Kovács, P Kiss, et al.Genetic Epidemiology|April 6, 2023
Effect of case and control definitions on genome-wide association study (GWAS) findingsMonica Isgut, Kijoung Song, Margaret G Ehm, et al.Genetic Epidemiology|April 9, 2021
A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statisticsKevin J Gleason, Fan Yang, Lin S ChenPageof 285