Document Type
Thesis
Degree
Master of Science
Major
Computer Science
Date of Defense
11-21-2024
Graduate Advisor
Sharlee Climer
Committee
Sharlee Climer
Mark Hauschild
Badri Adhikari
Abstract
Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute a vector-based measure for useful network construction. We present compelling results for our application towards quantifying statistically significant, synchronized genetic interactions associated with late-onset Alzheimer’s disease. In contrast with established measures, we observed unprecedent pattern association odds ratios following independent validation, indicating our developed metric renders enhanced perceptivity and encapsulation of complex trait heterogeneity. We offer our thorough network composition and validation analyses for extension into related and unspecified domain applications.
Recommended Citation
Valleroy, Zachary, "Holistic Correlation Measure for Enhanced Encapsulation of Trait Heterogeneity and Discovery of Co-expression" (2024). Theses. 468.
https://irl.umsl.edu/thesis/468