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The Annals of Applied Statistics|October 24, 2023
PREDICTION OF HEREDITARY CANCERS USING NEURAL NETWORKSBy Zoe Guan, Giovanni Parmigiani, Danielle Braun, et al.Bioinformatics (Oxford, England)|November 27, 2020
Robustifying genomic classifiers to batch effects via ensemble learningYuqing Zhang, Prasad Patil, W Evan Johnson, et al.The Annals of Applied Statistics|October 24, 2022
Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensingGabriel Loewinger, Prasad Patil, Kenneth T Kishida, et al.Journal of the National Cancer Institute|November 29, 2007
Breast cancer risk among male BRCA1 and BRCA2 mutation carriersYu Chuan Tai, Susan Domchek, Giovanni Parmigiani, et al.Bioinformatics (Oxford, England)|July 26, 2014
Más-o-menos: a simple sign averaging method for discrimination in genomic data analysisSihai Dave Zhao, Giovanni Parmigiani, Curtis Huttenhower, et al.Biometrics|April 23, 2015
Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear modelsChi Wang, Francesca Dominici, Giovanni Parmigiani, et al.Genetic Epidemiology|June 8, 2020
Practical implementation of frailty models in Mendelian risk predictionTheodore Huang, Malka Gorfine, Li Hsu, et al.The International Journal of Biostatistics|March 13, 2010
Likelihood estimation of conjugacy relationships in linear models with applications to high-throughput genomicsBrian S Caffo, Dongmei Liu, Robert B Scharpf, et al.Statistics in Medicine|January 26, 2025
Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer RiskThanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, et al.Biometrics|May 31, 2024
Bayesian meta-analysis of penetrance for cancer riskThanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, et al.Pageof 24