Quick Search:

Machine-learning a virus assembly fitness landscape

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 (Unpublished)

1901.05051.pdf - Draft Version

| Preview
Related URLs:


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Status: Unpublished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