Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Improving machine learning techniques for influenza-A classification
Influenza-A's ability to mutate constantly has resulted in recurring seasonal epidemics and pandemics. Recently, the virus's spread has been enhanced by its ability to infect multiple hosts simultaneously. Fast identification of the subtype and hosts of Influenza-A virus, is thus crucial, to quickly...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
AUC Knowledge Fountain
2016
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Improving machine learning techniques for influenza-A classification
- A bioinformatic study on the feasibility of a cross-species proteomics analyses of mycobacteria
- The identification of cytotoxic T lymphocyte (CTL) escape in a large, longitudinal subtype C HIV-1 sequence dataset
- Integration and visualisation of data in bioinformatics
- The evolutionary impacts of secondary structures within genomes of eukaryote-infecting single-stranded DNA viruses
- Identification of the virulence gene of Mycobacterium tuberculosis
- Evaluating the predictive performance of cytotoxic T lymphocyte epitope prediction tools using Elispot assay data