The application relates to a
gene design and evolutionary path
inference method based on an adaptive band constraint deep model. First, a random
gene sequence set is generated and converted into an evolvability vector containing evolutionary information, and the
mutation robustness thereof is calculated. A deep auto-encoding network is constructed,
model parameters are optimized through prototype clustering and small
batch training, the evolvability vector is encoded into a low-dimensional representation, and an evolvability space is obtained. Then, a deep regression model is established to predict the
protein expression level corresponding to the
gene sequence. The low-dimensional vector and the expression value are combined into a low-dimensional
tensor, the expression value is mapped into fitness through Monte Carlo sampling, and then an adaptive fitness
topographic map showing the evolutionary track is generated. Finally, the
tensor is stained according to the
mutation robustness, and the
gene sequence with the target property is screened. The scheme improves the gene design efficiency, and solves the problem that the fitness
topography is difficult to represent the
genotype evolutionary information.