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dc.contributor.authorTran, Paul
dc.date.accessioned2020-04-16T13:00:48Z
dc.date.available2020-04-16T13:00:48Z
dc.date.issued2020-04
dc.identifier.urihttp://hdl.handle.net/10675.2/623247
dc.descriptionRecord is embargoed until 04/16/2022
dc.description.abstractIntroduction: A major aim of modern medicine is to translate basic genomics findings using machine learning and other data analysis methods into clinical tests for improving patient care. Herein, I applied machine learning methods to publicly available genetic and genomic data to address three clinical problems in cancer and type 1 diabetes (T1D) research. Project 1: Cancer classification mostly depends on the anatomic pathology workforce; hence, diagnosis is slow, stepwise, and prone to errors and systemic bias. Using a transcriptome-based cancer classification method, I reconciled the 18% disagreement rate between histology and mutation-based classifier for brain cancer. Project 2: I applied the same transcriptome-based classification method to lung adenocarcinoma and identified 3 novel subgroups comprising ~30% of lung adenocarcinoma. Project 3: The estimated genetic heritability of T1D is up to 80%. Identifying those most genetically susceptible to T1D can lead to reduction of the number of islet autoimmunity cases and the number diabetic ketoacidosis episodes. I developed a genetic risk prediction model using neural networks which performs better than currently published methods. I applied model interpretation methods to the neural network and identified important genetic drivers for characterizing T1D molecular subgroups. Conclusion: These projects are small steps in translating genomic medicine projects to clinical applications but represent a future with more objective and automated tools to aid in clinical decision making.
dc.publisherAugusta University
dc.rightsCopyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law.
dc.subjectBioinformatics
dc.titleAPPLICATIONS OF MACHINE LEARNING TO GENOMICS: STUDIES IN TYPE 1 DIABETES AND CANCER
dc.typedissertationen_US
dc.typedissertationen
dc.contributor.departmentCenter for Biotechnology and Genomic Medicine
dc.language.rfc3066en
dc.date.updated2020-04-16T13:00:49Z
dc.description.advisorShe, Jin-Xiong
dc.description.degreeDoctor of Philosophy (PhD)
dc.description.committeeMcIndoe, Richard
dc.description.committeePurohit, Sharad
dc.description.committeeSharma, Ashok
dc.description.committeeAlbo, Daniel
dc.description.embargo04/16/2022


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