The invention relates to a microbial strain
DNA (deoxyribonucleic acid) mixed coding and an alternating
Transformer depth prediction method for
gene expression of the microbial strain
DNA mixed coding, and belongs to the crossing field of
bioinformatics and
artificial intelligence. The core of the method is to construct a
deep learning model capable of efficiently capturing
DNA sequence characteristics, and end-to-end accurate prediction of the expression level of a
target gene in a specific
microbial host is realized. The method comprises the following steps: carrying out binary coding on four conventional basic groups of DNA of a target strain, splicing eight key biological characteristics closely related to
gene expression, and carrying out
hybrid coding to generate a combined
characteristic matrix; a
deep learning network containing an alternating
Transform coding structure is established,
local structure information of
a DNA combination
feature matrix and a global context dependency relationship are deeply fused, and the analysis precision of
microorganism DNA sequence data and the prediction accuracy of a
gene expression level are improved. According to the technical scheme, an efficient and accurate computer-aided prediction tool is provided for optimization design of genetic elements (such as promoters) and gene sequences, the expensive experiment
trial and error cost and the strain characterization cost can be remarkably reduced, and the research and development process of high-yield strains is accelerated.