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Computational Approaches for Addressing Complexity In Biomedicine


Abstract 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 Mode... (more)
Created Date 2012
Contributor Brown, Justin Reed (Author) / Dinu, Valentin (Advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Subject Statistics / Biostatistics / Bioinformatics
Type Doctoral Dissertation
Extent 178 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Biomedical Informatics 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis