Developing methods for identifying associations in high-dimensional data and evaluating the methods to detect these in both statistical simulations and real data applications is an area of growing importance in many domains of biomedical science. Network analysis is becoming increasingly recognized as a vital tool for analyzing high-dimensional biomedical data in order to: 1) understand the complex interaction of factors in a single dataset, 2) enable integration of heterogeneous datasets in order to elucidate the impact of factors from one dataset on the other, and 3) predict outcomes based on our understanding of complex structures of variables within datasets. In this thesis, I developed statistical methods to address these issues and tested them through simulations of generalized cases and applied them to real data from New Hampshire Birth Cohort Study. Thus, the goals of the thesis were to: (1) develop a unsupervised network method that better identifies patterns of dependency and association of features in the data and apply this method to concentrations of 24 trace elements measured in the human placentas, (2) detect the impact of placental trace elements on the interacting network of cord-blood metabolites by developing a data integration method, and (3) predict birthweight using complex structures of elemental and metabolomic data by comparing prediction methods with different grouping strategies. The methods developed here can be applied to analyze complex mechanism of other similarly structured biomedical data (e,g, environmental exposures or metabolomes).
|Advisor:||Gui, Jiang, Karagas, Margaret R.|
|Commitee:||Romano, Megan E., Moen, Erika L., Li, Hongzhe|
|Department:||Quantitative Biomedical Sciences|
|School Location:||United States -- New Hampshire|
|Source:||DAI-B 81/2(E), Dissertation Abstracts International|
|Subjects:||Bioinformatics, Epidemiology, Biostatistics|
|Keywords:||Environmental mixtures, Exposomics, Gaussian graphical model, High-dimensional data, Lasso, Systemic network|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be