Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Utilising machine learning techniques on simulated viral evolution datasets to improve viral recombinant identification
This thesis explores machine learning applications for enhancing viral recombination detection. Using SANTA-SIM-generated viral evolution data, multiple computational approaches were developed and evaluated against existing methods in the Recombination Detection Program (RDP5). The study trained and...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English English |
| Published: |
Computational Biology Division
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Utilising machine learning techniques on simulated viral evolution datasets to improve viral recombinant identification
- Simulating recombinant sequence date to evaluate and improve computational methods of multiple sequence alignment and recombinant identification
- The viral protein R in HIV-associated acute ischaemic stroke: a case-control study
- Viral diversity, ecological interconnectedness, and the identification of mammalian chuviruses in Australian microbats
- HIV-1 strain-specific neutralizing antibody responses and the dynamics of viral evolution
- Identification of evolutionarily conserved nucleic acid secondary structures within viral genomes
- Evolution and viral properties of the SARS-CoV-2 BA.3.2 subvariant