A Machine Learning and Networks Approach to Infer Disease Mechanisms
| dc.contributor.advisor | Baranova, Ancha | |
| dc.creator | Kural, Kamil Can | |
| dc.date.accessioned | 2023-04-10T18:46:31Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | All biological problems, including cancer, mental disorders, rare diseases, and immunological disorders, are complex multisystem pathophysiological shifts facilitated by functional changes. Multiple points of origin and mechanisms can contribute, including genetic mutations, epigenetic factors, protein misfolding, and differential gene expression, which promote such functional abnormalities. This dissertation describes mega-analysis and machine learning methods to identify and select essential entities that are prime candidates for drivers of such disorganization. Furthermore, heterogeneous networks have been employed to achieve a more thorough pathway analysis that takes protein-protein interactions into account. This dissertation contains four projects, investigating PPARD’s role in major depressive disorder, exploring the link between schizophrenia and myocardial infarctions, identifying the connections between atopic dermatitis and major depressive disorder, and finally, biomarker identification for survival status prediction of patients with brain cancer. | |
| dc.description.embargo | 2024-08-31 | |
| dc.description.note | This work is embargoed by the author and will not be publicly available until 2024-08-31. | |
| dc.format.extent | 201 pages | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier.uri | https://hdl.handle.net/1920/13252 | |
| dc.language.iso | en | |
| dc.rights | Copyright 2022 Kamil Can Kural | |
| dc.rights.uri | https://rightsstatements.org/vocab/InC/1.0 | |
| dc.subject | Biomarker Discovery | |
| dc.subject | Graph Databases | |
| dc.subject | Machine learning | |
| dc.subject | Mage-Analysis | |
| dc.subject | Pathway Analysis | |
| dc.subject | Protein-Protein Interactions | |
| dc.subject.keywords | Bioinformatics | |
| dc.subject.keywords | Computer science | |
| dc.subject.keywords | Statistics | |
| dc.title | A Machine Learning and Networks Approach to Infer Disease Mechanisms | |
| dc.type | Text | |
| thesis.degree.discipline | Bioinformatics and Computational Biology | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Ph.D. in Bioinformatics and Computational Biology |