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dc.contributor.author Zhao, Xin en_US
dc.date.accessioned 2011-07-21T23:06:44Z en_US
dc.date.available 2011-07-21T23:06:44Z en_US
dc.date.issued 2008 en_US
dc.identifier.isbn 9780549787891 en_US
dc.identifier.uri http://hdl.handle.net/10125/20510 en_US
dc.description Thesis (Ph.D.)--University of Hawaii at Manoa, 2008. en_US
dc.description DNA microarray technology has provided researchers a high-throughput means to simultaneously measure expression levels for thousands of genes in an experiment. With a probit regression setting and assuming that the link function between significant gene expression data and latent variable for the response label is a Gaussian process, a kernel-induced hierarchical Bayesian framework is built for a cancer classification problem by using microarray gene expression data. en_US
dc.description In summary, built on a Gaussian process model, a kernel-induced hierarchical Bayesian framework using microarray gene expression data for a cancer multi-classification problem is presented in this study. Our main contribution is a fully automated learning algorithm to solve this Bayesian model. Satisfactory results have been achieved in both the simulated examples and the real-world data studies. en_US
dc.description Six published microarray datasets were analyzed in this study. The results show that predictive performance of our method for all these datasets is better than or at least as good as that of other state-of-the-art microarray analysis methods. Our method especially shows its superiority in analyzing one dataset that contains multiple suspicious mislabeled samples. For each of these datasets, we identified a set of significant genes, which can be used for further biological inspection at genome level. en_US
dc.description Targeting a multi-classification problem and adopting a variable selection approach with a Gibbs sample as core, we developed the algorithm, kernel-imbedded Gaussian Process (KIGP), to analyze microarray data under a Bayesian framework. Through a feature projection procedure and using a univariate ranking scheme as gene-selection strategy, we further designed an alternative microarray analysis model, natural kernel-imbedded Gaussian Process (NKIGP). In the end, embedded with a reversible jump Markov chain Monte Carlo (RJMCMC) model, we present an efficient algorithm with a cascading structure to unify the proposed methods of this study. en_US
dc.description The simulated examples demonstrate that, our method performs almost always close to the Bayesian bound in both the cases with linear Bayesian classifiers and the cases with very non-linear Bayesian classifiers. Even with mislabeled training samples, our method is still robust, showing its broad usability to those microarray analysis problems that linear methods may work flakily. en_US
dc.description Includes bibliographical references (leaves xxx-xxx). en_US
dc.description Also available by subscription via World Wide Web en_US
dc.description 179 leaves, bound 29 cm en_US
dc.language.iso en-US en_US
dc.relation Theses for the degree of Doctor of Philosophy (University of Hawaii at Manoa). Computer Science; no. 5137 en_US
dc.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner. en_US
dc.title Bayesian learning framework with kernel-imbedded Gaussian processes applied to microarray analysis en_US
dc.type Thesis en_US
dc.type.dcmi Text en_US

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