Year: 2010
Type: dissertation
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\nprotein 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\nproteomic 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\nfunctions 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\nessential genes and study their predicted novel functions.
Source: TSpace (University of Toronto)
Author: Renqiang Min
Institution: University of Toronto
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2182006876