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• W162932699 abstract "Title of thesis: DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS Xia Li, Master of Public Health, 2012 Directed by: Assistant Professor Tongtong Wu Department of Epidemiology and Biostatistics This research aims to integrate linear structures of genetic networks into genomewide analysis studies (GWAS). Lasso penalized logistic regression is ideally suited for continuous model selection in case-control disease gene mapping, especially when the number of predictor variables far exceeds the number of observations. But it fails to consider the structure of genetic networks. Imposing an additional weighted fused lasso can further remove irrelevant predictors. Nesterov’s method is employed to handle the high dimensionality and complexity of genetic data. It also resolves the non-differentiability problem of the lasso and fused lasso penalties. In simulation studies, this proposed method shows advantages in some cases compared with lasso and fused lasso. We apply this method to the coeliac data on chromosome 8. DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS By Xia Li Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park, in partial fulfillment of the requirements for the degree of Master of Public Health 2012 Advisory Committee: Assistant Professor Tongtong Wu, Chair Assistant Professor Guangyu Zhang Associate Professor Paul J. Smith c © Copyright by Xia Li 2012 Table of" @default.
• W162932699 created "2016-06-24" @default.
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• W162932699 date "2012-01-01" @default.
• W162932699 modified "2023-09-22" @default.
• W162932699 title "DOUBLY PENALIZED LOGISTIC REGRESSION FOR GENOMEWIDE ASSOCIATION STUDIES WITH LINEARLY STRUCTURED GENETIC NETWORKS" @default.
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