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Permanent link (DOI): https://doi.org/10.7939/R3893K

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Bio-relation Discovery and Sparse Learning Open Access

Descriptions

Other title
Subject/Keyword
Sparse Learning
Microarray
Bi-cluster
Kernelization
Bio-relation
Gene Regulatory Network
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Shi, Yi
Supervisor and department
Dale Schuurmans (Computing Science)
Guohui Lin (Computing Science)
Examining committee member and department
Xianghong Zhou (Biological Sciences and Computer Science, University of Southern California)
Liang Li (Chemistry)
Randy Goebel (Computing Science)
Russell Greiner (Computing Science)
Department
Department of Computing Science
Specialization
Computational Biology and Machine Learning
Date accepted
2012-08-08T14:09:54Z
Graduation date
2012-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
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.
Language
English
DOI
doi:10.7939/R3893K
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. 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 author's prior written permission.
Citation for previous publication
Y. Shi, X. Liao, X. Zhang, G.Lin, D. Schuurmans. Sparse Learning based Linear Coherent Bi-clustering. WABI 2012 (WABI). Lecture Notes in Bioinformatics 7534, 346-364, 2012.Y. Shi, M. Hasan, Z. Cai, G.Lin, and D. Schuurmans. Linear Coherent Bi-clustering via Beam Searching and Sample Set Clustering. Discrete Mathematics, Algorithms and Applications. World Scientific Publishing (DMAA). Volume 4, Issue 2, 2012.Y. Shi, Y. Guo, G.Lin, and D. Schuurmans. Kernel-based Gene Regulatory Network Inference. International Conference on Computational Systems Bioinformatics (CSB 2010). Stanford, California, United States. August 16-18, 2010. Pages 156-165Y. Shi, M. Hasan, Z. Cai, G.Lin, and D. Schuurmans. Linear Coherent Bi-cluster Discovery via Beam Detection and Sample Set clustering. International Conference on Combinatorial Optimization and Applications (COCOA 2010). The Big Island, Hawaii, United States. December 18-20, 2010.

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