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Bio-relation Discovery and Sparse Learning

  • Author / Creator
    Shi, Yi
  • In this dissertation, I discuss several important problems in the area of bio-relation discovery (BRD). Discovering bio-relations is an important problem that arises frequently in bioinformatics. It involves identifying relationships (usually pairwise) between bio-entities. These relationships can be categorized as undirected versus directed. I will investigate both types of BRD problems in this dissertation. For undirected BRD, I will specifically discuss the gene-sample expression bi-clustering problem. For directed BRD, I will focus on problems in gene regulatory network inference and drug-target network inference. For all these problems I have investigated both heuristic approaches and sparse learning based approaches from machine learning.

  • Subjects / Keywords
  • Graduation date
    2012-11
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3893K
  • 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.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Computing Science
  • Specialization
    • Computational Biology and Machine Learning
  • Supervisor / co-supervisor and their department(s)
    • Dale Schuurmans (Computing Science)
    • Guohui Lin (Computing Science)
  • Examining committee members and their departments
    • Liang Li (Chemistry)
    • Russell Greiner (Computing Science)
    • Xianghong Zhou (Biological Sciences and Computer Science, University of Southern California)
    • Randy Goebel (Computing Science)