The invention discloses a training method for decoupling into a
drug response migration prediction model, and the method comprises the following steps: a
drug specificity training step, extracting, using a
drug-specific
encoder, a feature shared between the first unlabeled
cell line sample data constituting the source domain and the first unlabeled clinical sample data constituting the target domain and associated with the particular drug response; a decoupling forming step: a decoupling forming step; a second
encoder capable of extracting a trained source domain of features independent of the particular drug response in second unlabeled
cell line sample data and a third
encoder capable of extracting a trained target domain of features independent of the particular drug response in second unlabeled clinical sample data by the drug-specific encoder the encoder is used for splicing the output of the drug specificity encoder and the output of the third encoder of the trained target domain, so as to obtain synthetic sample data; the
synthetic data is then used to
train the drug-specific encoder, thereby obtaining the trained drug-specific encoder, the method and apparatus decouple the
cell line and clinical inputs into features related and independent of drug response and innovatively utilize the
synthetic data to augment the dataset, and the method and apparatus can be used to identify the drug-specific encoder. And the drug sensitivity of clinical
cancer patients is accurately predicted.