test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works
       

Advanced Search
Home   
 
Browse   
Communities
& Collections
  
Issue Date   
Author   
Title   
Subject   
 
Sign on to:   
Receive email
updates
  
My Account
authorized users
  
Edit Profile   
 
Help   
About T-Space   

T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Doctoral >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/26209

Title: Machine Learning Approaches to Biological Sequence and Phenotype Data Analysis
Authors: Min, Renqiang
Advisor: Bonner, Anthony
Zhang, Zhaolei
Department: Computer Science
Keywords: Machine Learning
Bioinformatics
Computational Biology
Issue Date: 17-Feb-2011
Abstract: To understand biology at a system level, I presented novel machine learning algorithms to reveal the underlying mechanisms of how genes and their products function in different biological levels in this thesis. Specifically, at sequence level, based on Kernel Support Vector Machines (SVMs), I proposed learned random-walk kernel and learned empirical-map kernel to identify protein remote homology solely based on sequence data, and I proposed a discriminative motif discovery algorithm to identify sequence motifs that characterize protein sequences' remote homology membership. The proposed approaches significantly outperform previous methods, especially on some challenging protein families. At expression and protein level, using hierarchical Bayesian graphical models, I developed the first high-throughput computational predictive model to filter sequence-based predictions of microRNA targets by incorporating the proteomic data of putative microRNA target genes, and I proposed another probabilistic model to explore the underlying mechanisms of microRNA regulation by combining the expression profile data of messenger RNAs and microRNAs. At cellular level, I further investigated how yeast genes manifest their functions in cell morphology by performing gene function prediction from the morphology data of yeast temperature-sensitive alleles. The developed prediction models enable biologists to choose some interesting yeast essential genes and study their predicted novel functions.
URI: http://hdl.handle.net/1807/26209
Appears in Collections:Doctoral

Files in This Item:

File Description SizeFormat
Min_Renqiang_201011_PhD_thesis.pdf.pdf1.25 MBAdobe PDF
View/Open

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

uoft