| Description |
Uncertainty Quantification is a growing field of study with critical implications in assessing the reliability of complex computational models. In the biomedical field, use of computational modeling and simulation is increasing in both research and clinical applications. Even with the inherent and ubiquitous uncertainties in its data, the biomedical domain has not seen an accompanying establishment of clear Uncertainty Quantification frameworks and best practices when compared to other computationally intensive fields. In this dissertation, a review of an Uncertainty Quantification framework is discussed with current applications and techniques presently implemented in the biomedical field. Afterward, the impact of uncertainty associated with self-reported family-health history on four frequently used breast and ovarian cancer risk prediction models is estimated using Monte Carlo simulations. Following this, we present an example of co-expression network analysis to identify genetic modules associated with Spinocerebellar Ataxia Type 2 disease pathogenesis. Lastly, we present an assessment of analytical and experimental design uncertainty on the identified gene co-expression network, using a systematic approach for multiple pipeline analysis and Monte Carlo simulations. |
