Optimization, random resampling, and modeling in bioinformatics

Ge, Weihao

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https://hdl.handle.net/2142/101707 Copy

Description

Title

Optimization, random resampling, and modeling in bioinformatics

Author(s)

Ge, Weihao

Issue Date

2018-07-12

Director of Research (if dissertation) or Advisor (if thesis)

• Jakobsson, Eric
• Mainzer, Liudmila S

Doctoral Committee Chair(s)

Jakobsson, Eric

Committee Member(s)

• Sinha, Saurabh
• Nelson, Mark
• McHenry, Kenton

Department of Study

School of Molecular & Cell Bio

Discipline

Biophysics & Computnl Biology

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(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.

Graduation Semester

2018-08

Type of Resource

text

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http://hdl.handle.net/2142/101707

Copyright and License Information

Copyright 2018 Weihao Ge

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