APPLICATIONS OF MACHINE LEARNING TO GENOMICS: STUDIES IN TYPE 1 DIABETES AND CANCER
dc.contributor.author | Tran, Paul | |
dc.date.accessioned | 2020-04-16T13:00:48Z | |
dc.date.available | 2020-04-16T13:00:48Z | |
dc.date.issued | 2020-04 | |
dc.identifier.uri | http://hdl.handle.net/10675.2/623247 | |
dc.description | Record is embargoed until 04/16/2022 | |
dc.description.abstract | Introduction: 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.publisher | Augusta University | |
dc.rights | Copyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law. | |
dc.subject | Bioinformatics | |
dc.title | APPLICATIONS OF MACHINE LEARNING TO GENOMICS: STUDIES IN TYPE 1 DIABETES AND CANCER | |
dc.type | dissertation | en_US |
dc.type | dissertation | en |
dc.contributor.department | Center for Biotechnology and Genomic Medicine | |
dc.language.rfc3066 | en | |
dc.date.updated | 2020-04-16T13:00:49Z | |
dc.description.advisor | She, Jin-Xiong | |
dc.description.degree | Doctor of Philosophy (PhD) | |
dc.description.committee | McIndoe, Richard | |
dc.description.committee | Purohit, Sharad | |
dc.description.committee | Sharma, Ashok | |
dc.description.committee | Albo, Daniel | |
dc.description.embargo | 04/16/2022 |