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

  • Sparse Learning
  • Microarray
  • Bi-cluster
  • Kernelization
  • Bio-relation
  • Gene Regulatory Network

• Graduation date

  Fall 2012

• 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.