The invention relates to a
gene design and evolution path
inference method based on a self-adaptive constrained depth model, which comprises the following steps: generating a random
gene sequence set through a preset
algorithm rule, converting the random
gene sequence set into an evolutionary sequence containing evolutionary information, namely an evolutionary vector, and calculating
mutation robustness; constructing a deep self-encoding
network model, dynamically determining the optimal prototype number of the network through small-batch iterative training by adopting a prototype clustering
analysis method, and performing
model parameter optimization based on training data; inputting the evolutionary vector into an
encoder module of an auto-
encoder to obtain a low-dimensional representation of the evolutionary vector, namely an evolutionary space; establishing a deep regression prediction model, and accurately calculating the
protein expression level corresponding to each regulatory
gene sequence; establishing a low-dimensional
tensor according to the obtained low-dimensional representation of the evolutionary vector and the corresponding expression value, performing Monte Carlo sampling in the low-dimensional
tensor, mapping the
protein expression value to the fitness, and generating a fitness contour map containing an evolution trajectory, namely obtaining a fitness
terrain containing evolution information; and
dyeing the low-dimensional
tensor by using
mutation robustness, and selecting a
gene sequence with required properties on the graph. According to the technical scheme, the efficiency problem of existing
gene sequence design is effectively solved, and the problem that the fitness
terrain cannot represent evolution information of each
genotype is effectively solved.