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dc.contributor.advisorChen, Xue-wen
dc.contributor.authorHan, Bing
dc.date.accessioned2012-03-01T20:13:22Z
dc.date.available2012-03-01T20:13:22Z
dc.date.issued2011-12-31
dc.date.submitted2011
dc.identifier.otherhttp://dissertations.umi.com/ku:11889
dc.identifier.urihttp://hdl.handle.net/1808/8781
dc.description.abstractTo understand the underlying molecular mechanisms of cancer and therefore to improve pathogenesis, prevention, diagnosis and treatment of cancer, it is necessary to explore the activities of cancer-related genes and the interactions among these genes. In this dissertation, I use machine learning and computational methods to identify differential gene relations and detect gene-gene interactions. To identify gene pairs that have different relationships in normal versus cancer tissues, I develop an integrative method based on the bootstrapping K-S test to evaluate a large number of microarray datasets. The experimental results demonstrate that my method can find meaningful alterations in gene relations. For gene-gene interaction detection, I propose to use two Bayesian Network based methods: DASSO-MB (Detection of ASSOciations using Markov Blanket) and EpiBN (Epistatic interaction detection using Bayesian Network model) to address the two critical challenges: searching and scoring. DASSO-MB is based on the concept of Markov Blanket in Bayesian Networks. In EpiBN, I develop a new scoring function, which can reflect higher-order gene-gene interactions and detect the true number of disease markers, and apply a fast Branch-and-Bound (B&B) algorithm to learn the structure of Bayesian Network. Both DASSO-MB and EpiBN outperform some other commonly-used methods and are scalable to genome-wide data.
dc.format.extent107 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectComputer science
dc.subjectBioinformatics
dc.subjectCancer
dc.subjectDifferential gene relations
dc.subjectGene-gene interactions
dc.subjectMachine learning
dc.subjectSystem biology
dc.titleDETECTING CANCER-RELATED GENES AND GENE-GENE INTERACTIONS BY MACHINE LEARNING METHODS
dc.typeDissertation
dc.contributor.cmtememberAgah, Arvin
dc.contributor.cmtememberGrzymala-Busse, Jerzy
dc.contributor.cmtememberHuan, Luke
dc.contributor.cmtememberDuncan, Tyrone
dc.contributor.cmtememberTalebizadeh, Zohreh
dc.thesis.degreeDisciplineElectrical Engineering & Computer Science
dc.thesis.degreeLevelPh.D.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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