dc.contributor.advisor | Chen, Xue-wen | |
dc.contributor.author | Han, Bing | |
dc.date.accessioned | 2012-03-01T20:13:22Z | |
dc.date.available | 2012-03-01T20:13:22Z | |
dc.date.issued | 2011-12-31 | |
dc.date.submitted | 2011 | |
dc.identifier.other | http://dissertations.umi.com/ku:11889 | |
dc.identifier.uri | http://hdl.handle.net/1808/8781 | |
dc.description.abstract | To 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.extent | 107 pages | |
dc.language.iso | en | |
dc.publisher | University of Kansas | |
dc.rights | This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author. | |
dc.subject | Computer science | |
dc.subject | Bioinformatics | |
dc.subject | Cancer | |
dc.subject | Differential gene relations | |
dc.subject | Gene-gene interactions | |
dc.subject | Machine learning | |
dc.subject | System biology | |
dc.title | DETECTING CANCER-RELATED GENES AND GENE-GENE INTERACTIONS BY MACHINE LEARNING METHODS | |
dc.type | Dissertation | |
dc.contributor.cmtemember | Agah, Arvin | |
dc.contributor.cmtemember | Grzymala-Busse, Jerzy | |
dc.contributor.cmtemember | Huan, Luke | |
dc.contributor.cmtemember | Duncan, Tyrone | |
dc.contributor.cmtemember | Talebizadeh, Zohreh | |
dc.thesis.degreeDiscipline | Electrical Engineering & Computer Science | |
dc.thesis.degreeLevel | Ph.D. | |
kusw.oastatus | na | |
kusw.oapolicy | This item does not meet KU Open Access policy criteria. | |
kusw.bibid | 7643173 | |
dc.rights.accessrights | openAccess | |