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Full-Text Articles in Physical Sciences and Mathematics
Genetics Of Pediatric Musculoskeletal Disorders, Lilian Antunes
Genetics Of Pediatric Musculoskeletal Disorders, Lilian Antunes
Arts & Sciences Electronic Theses and Dissertations
Pediatric musculoskeletal disorders are an extremely broad category of diseases that are often inherited. While individually rare, collectively these disorders are common, affecting around 3% of live births in the US. Despite the mounting clinical and molecular evidence for a genetic etiology, the cause for many patients with pediatric musculoskeletal disorders remain largely unknown. Major challenges in rare pediatric diseases include recruiting large numbers of patients and determining the significance and functional impacts of variants associated with disease within individuals or families. Whole exome sequencing (WES) is a powerful tool to identify coding variants that are associated with rare pediatric …
Lead Poisoning In United States Children, Zeren Zhou
Lead Poisoning In United States Children, Zeren Zhou
Arts & Sciences Electronic Theses and Dissertations
We investigate factors related to blood lead levels of children ages 1 to 5 in the United States for the years 2007-2014. We use data from the National Health and Nutrition Examination Survey (NHANES). The goal is to explore predictors of lead in childrens' blood and to develop a multivariate model using as many predictors as possible. The analysis is conducted using SAS survey regression procedures that account for weighting, stratification, and clustering of the data.
Application Of Machine Learning To Mapping And Simulating Gene Regulatory Networks, Hien-Haw Liow
Application Of Machine Learning To Mapping And Simulating Gene Regulatory Networks, Hien-Haw Liow
Arts & Sciences Electronic Theses and Dissertations
This dissertation explores, proposes, and examines methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene regulation relations; a parametric model is developed to predict geneexpression levels solely from genotype information. Simulation of network behavior is shown to complement regression analysis greatly in capturing the dynamics of gene regulatory networks. Finally, as an application and extension of novel approaches in gene expression analysis, new methods of discovering topological structure of gene …