Abstract
This dissertation explores, proposes, and examines methods of applying modernmachine learning and Bayesian statistics in the quantitative and qualitative modeling of gene regulatory networks using high-throughput gene expression data. A semi-parametric Bayesian model based on random forest is developed to infer quantitative aspects of gene regulation relations; a parametric model is developed to predict geneexpression levels solely from genotype information. Simulation of network behavior is shown to complement regression analysis greatly in capturing the dynamics of gene regulatory networks. Finally, as an application and extension of novel approaches in gene expression analysis, new methods of discovering topological structure of gene regulatory networks are developed and shown to provide improvement over existing methods.
Committee Chair
Edward Spitznagel
Committee Members
Barak Cohen, Renato Reres, Victor Wickerhauser
Degree
Doctor of Philosophy (PhD)
Author's Department
Mathematics
Document Type
Dissertation
Date of Award
Spring 5-15-2015
Language
English (en)
DOI
https://doi.org/10.7936/K73776WC
Recommended Citation
Liow, Hien-haw, "Application of Machine Learning to Mapping and Simulating Gene Regulatory Networks" (2015). Arts & Sciences Theses and Dissertations. 405.
The definitive version is available at https://doi.org/10.7936/K73776WC
Comments
Permanent URL: https://doi.org/10.7936/K73776WC