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 (Unpublished)

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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Status:	Unpublished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

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