Application of Machine Learning to Mapping and Simulating Gene Regulatory Networks

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mathematics

Year dc:date.available

2015

Author dc:creator

• Liow, Hien-haw

Contributors dc:contributor

• Edward Spitznagel
• Barak Cohen, Renato Reres, Victor Wickerhauser

• Bayesian
• Gene expression
• Gene regulation
• Machine Learning
• Network
• Statistics
• Mathematics

Statement dc:rights

• I have not registered my thesis with the U.S. Copyright Office, and do not intend to.

Language dc:language

English (en)

Identifier

https://openscholarship.wustl.edu/art_sci_etds/405

OAI identifier oai:identifier

oai:openscholarship.wustl.edu:art_sci_etds-1405