Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
Details
Title
- Computational approaches for addressing complexity in biomedicine
Contributors
- Brown, Justin Reed (Author)
- Dinu, Valentin (Thesis advisor)
- Johnson, William (Committee member)
- Petitti, Diana (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2012
Resource Type
Collections this item is in
Note
- thesisPartial requirement for: Ph.D., Arizona State University, 2012
- bibliographyIncludes bibliographical references (p. 163-169)
- Field of study: Biomedial informatics
Citation and reuse
Statement of Responsibility
by Justin Reed Brown