A study of computational methods to analyze gene expression data

Ko, Youn Hee

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https://hdl.handle.net/2142/18574 Copy

Description

Title

A study of computational methods to analyze gene expression data

Author(s)

Ko, Youn Hee

Issue Date

2011-01-21T22:47:03Z

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

Zhai, ChengXiang

Doctoral Committee Chair(s)

Zhai, ChengXiang

Committee Member(s)

• Rodriguez-Zas, Sandra L.
• Price, Nathan D.
• Lu, Xinghua

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Gene network
• Bayesian network
• Bayesian mixture model
• Principal component analysis (PCA)
• Markov chain Monte Carlo (MCMC)
• in situ hybridization
• brain
• Eigen-brain
• cell-type specificity

Abstract

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

Graduation Semester

2010-12

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http://hdl.handle.net/2142/18574

Copyright and License Information

Copyright 2010 Youn Hee Ko

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