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Permanent link (DOI): https://doi.org/10.7939/R3MS3K880

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Development of Statistical Methods for Analysis of High-Dimensional Biological Data Open Access

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Other title
Subject/Keyword
E. coli host specificity
high-dimensional biological data
logic regression
image analysis
automatic TB detection
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Li, Qiaozhi
Supervisor and department
Yasui, Yutaka (Public Health Sciences)
Examining committee member and department
Jhangri, Gian (Public Health Sciences)
Neumann, Norman (Public Health Sciences)
Yasui, Yutaka (Public Health Sciences)
Department
Department of Public Health Sciences
Specialization
Epidemiology
Date accepted
2013-09-28T21:01:54Z
Graduation date
2013-11
Degree
Master of Science
Degree level
Master's
Abstract
High-dimensional biological data have been increasingly made available for tackling complex health problems. As with any Big Data opportunities, this has led to methodological challenges for extracting relevant information from such data, particularly in settings where biologically-sensible and statistically-appropriate methodologies that are practical and effective in public health practice or healthcare delivery have not been established. This thesis aims at developing statistical methods specifically for two heath problems with high-dimensional biological data: I) A logic-regression-based genetic biomarker discovery method for environmental health, identifying the source/host of Escherichia coli using its genomic data; and II) An image analysis method for automatic tuberculosis (TB) detection in resource-limited settings, where the modern TB detection methods are not employable, using high-throughput sputum-culture images. My research has developed these methods that are aimed to be implemented in the respective fields to advance effectiveness of the public health practice.
Language
English
DOI
doi:10.7939/R3MS3K880
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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