| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Kemmeren, Patrick | |
| dc.contributor.author | Nijs, Nathalie | |
| dc.date.accessioned | 2023-04-15T00:00:44Z | |
| dc.date.available | 2023-04-15T00:00:44Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/43786 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | Cancer is a very complex disease driven by DNA alternations, called somatic mutations. The identification of cancer-related genes and their contribution to the initiation and development of cancer is needed to make an accurate diagnosis and treatment. For this reason, high throughput DNA sequencing techniques are frequently applied, resulting in dozens or even hundreds of cancer driver candidate genes. However, identifying a small number of cancer driver mutation genes from a much greater number | |
| dc.title | Computational tools for prioritizing cancer driver candidates | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Bioinformatics and Biocomplexity | |
| dc.thesis.id | 15782 | |
