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E. coli host specificity, high-dimensional biological data, logic regression, image analysis, automatic TB detection

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.





Author: Li, Qiaozhi

Source: https://era.library.ualberta.ca/


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University of Alberta Development of Statistical Methods for Analysis of High-Dimensional Biological Data by Qiaozhi Li A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Epidemiology Department of Public Health Sciences ©Qiaozhi Li Fall 2013 Edmonton, Alberta 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.
Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. 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 authors prior written permission. Dedication I would like to dedicate this thesis to my husband, Feng Dai, for offering me constant encouragement, understanding and support, to my daughter, Zihan Dai, for her lovely spirit bringing endless joy to my life, and to my parents, Miaolin Li and Guizhen Liu, for always being positive and supportive about my goals and ambitions. 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-...





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