The invention relates to the technical field of
artificial intelligence, in particular to a wheat
abiotic stress character SNP (
Single Nucleotide Polymorphism) prediction method based on an automatic CNN (
Convolutional Neural Network) model, which comprises the following steps of: collecting GWAS data related to wheat from a GWAS Atlas
database, and constructing a high-quality
mutation site
data set; performing
feature extraction on the
data set by using
a DNA original sequence, text-based features and structure-based features, sorting the importance of input features by using an SHAP method, assisting
feature selection, and explaining the result of the prediction model; then, an automatic architecture search framework is constructed, automatic neural architecture search is adopted, the model is evolved in a specified search space, the optimal performance is achieved on a
verification set, and the accuracy, the AUPR and the AUC of the model are evaluated on a
test set. The invention provides a
deep learning framework which is efficient, accurate and explainable, and is used for predicting the SNP related to the wheat
biological stress, so that the development of wheat
stress resistance breeding and
genomics research is promoted.