Dechant, Pierre-Philippe 
ORCID: https://orcid.org/0000-0002-4694-4010 and He, Yang-He
(2019)
Machine-learning a virus assembly fitness
landscape.
arXive.
(Unpublished)
|
Text
1901.05051.pdf - Draft Version | Preview |
Abstract
Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with 12 corresponding packaging signals in three affinity bands. This whole genome/phenotype space consisting of 312 genomes has been explored via computationally expensive stochastic assembly models, giving a fitness landscape in terms of the assembly efficiency. Using latest machine-learning techniques by establishing a neural network, we show that the intensive computation can be short-circuited in a matter of minutes to astounding accuracy.
| Item Type: | Other |
|---|---|
| Additional Information: | arXiv:1901.05051 |
| Status: | Unpublished |
| Subjects: | Q Science > Q Science (General) > Q325 Machine learning Q Science > QA Mathematics |
| School/Department: | School of Science, Technology and Health |
| URI: | https://ray.yorksj.ac.uk/id/eprint/3681 |
University Staff: Request a correction | RaY Editors: Update this record
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
