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Description

Title:Optimization, random resampling, and modeling in bioinformatics
Author(s):Ge, Weihao
Director of Research:Jakobsson, Eric; Mainzer, Liudmila S
Doctoral Committee Chair(s):Jakobsson, Eric
Doctoral Committee Member(s):Sinha, Saurabh; Nelson, Mark; McHenry, Kenton
Department / Program:School of Molecular & Cell Bio
Discipline:Biophysics & Computnl Biology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):resampling
gene ontology
pathway
FDR
GWAS
epistasis
search space reduction
Abstract:Quantitative phenotypes regulated by multiple genes are prevalent in nature and many diseases falls into this category. High-throughput sequencing and high-performance computing provides a basis to understand quantitative phenotypes. However, finding a statistical approach correctly model the phenotypes remain a challenging problem. In this work, I present a resampling-based approach to obtain biological functional categories from gene set and apply the approach to analyze lithium-sensitivity of neurological diseases and cancer. Then, the non-parametrical permutation-based approach is applied to evaluate the performance of a GWAS modeling procedure. While the procedure performs well in statistics, search space reduction is required to address the computation challenge.
Issue Date:2018-07-12
Type:Thesis
URI:http://hdl.handle.net/2142/101707
Rights Information:Copyright 2018 Weihao Ge
Date Available in IDEALS:2018-09-27
Date Deposited:2018-08


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