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Gaussian Graphical Models For Omics Data: New Methodology And Applications, Katherine H. Shutta
Gaussian Graphical Models For Omics Data: New Methodology And Applications, Katherine H. Shutta
Doctoral Dissertations
Gaussian graphical models (GGMs) are useful network estimation tools for modeling direct dependencies that characterize multivariate data. The GGM modeling framework is one way to elucidate complex systems-level properties that can be difficult to detect in univariate analyses. In this dissertation, we begin by presenting a tutorial and review of the current state of the field of GGM theory and application. Next, we present a motivating application of GGMs in a study of metabolomic networks associated with chronic distress in women in the Women's Health Initiative (WHI) and in the Nurses' Health Study cohorts. In the third chapter, we present …