Journal
PLOS ONE
Volume 16, Issue 5, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0250227
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Funding
- Science and Technology Facilities Council (STFC) [ST/J00037X/1]
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The study simplifies computationally expensive stochastic assembly models using a neural network, allowing for the quick determination of the fitness landscape in terms of assembly efficiency in just a matter of minutes with astounding accuracy.
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 3(12) 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.
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