Computer Science ETDs

Publication Date

Summer 7-15-2024

Abstract

Complex systems are comprised of different components. Interactions and associations among these components define the functionality of the system. For example, T cells must directly interact with virally infected cells to kill them. This research characterizes the most relevant components of complex systems by analyzing interacting relationships using information theoretic measures. It emphasizes the importance of spatial and temporal dynamics, which occur when components share spatial proximities or temporal sequences. Novel information theoretic analyses are proposed for quantifying the degree of association among system components, which is key to defining the spatiotemporal dynamics. One focus of this work is the application of these measures to biomedical datasets, bridging the gap between computational science and life sciences. Another focus is on the visual representation of such interactions, providing a new scientific lens to understand relevant features of complex systems. The measures are validated against benchmarks to ensure efficacy and applicability across multidisciplinary fields. This work advances the fields of computational biology and scientific visualization by providing novel, robust tools to analyze and interpret complex spatiotemporal interactions.

Language

English

Keywords

information theory, mutual information, computational biology, spatial cell association and interactions, complex systems, data visualization, feature extraction, modeling and simulation, SARS-CoV-2, CT scan analysis

Document Type

Dissertation

Degree Name

Computer Science

Level of Degree

Doctoral

Department Name

Department of Computer Science

First Committee Member (Chair)

Melanie E. Moses

Second Committee Member

Shuang Luan

Third Committee Member

Judy Cannon

Fourth Committee Member

George Matthew Fricke

Fifth Committee Member

Soumya Dutta

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