| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Ridder, Jeroen | |
| dc.contributor.author | Wilke, Yano | |
| dc.date.accessioned | 2024-04-04T23:01:50Z | |
| dc.date.available | 2024-04-04T23:01:50Z | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/46245 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | PyroTimer is a Bayesian mixture model designed to time copy number gains in tumor evolution, using the probabilistic programming framework Pyro. It leverages Hamiltonian Monte Carlo and Stochastic Variational Inference for algorithmic inference, and its efficacy is validated on both simulated and real-world data, including comparisons with the MutationTimeR tool. | |
| dc.title | PyroTimer is a performant tool to time copy number gains | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Molecular and Cellular Life Sciences | |
| dc.thesis.id | 29752 | |
