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

• Author / Creator

  Li, Qiaozhi

• 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.

• Subjects / Keywords

  • Image analysis
  • E. coli host specificity
  • Logic regression
  • High-dimensional biological data
  • Automatic TB detection

• Graduation date

  Fall 2013

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3MS3K880

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.