The invention discloses a
drug-induced autoimmune response prediction method based on
oversampling and classification modeling. The method comprises the steps that a
drug-induced autoimmune response
data set is acquired and preprocessed; the SNN-DPC is adopted to cluster the preprocessed
data set; according to the JS
divergence, selecting and marking clusters with class distribution similar to
global distribution, and calculating the number of samples needing to be generated for the marked clusters based on a Kulczynski index and
Gaussian density; synthesizing an
oversampling sample in the mark cluster through density weighted three-point interpolation; performing LOF
noise removal on the synthetic samples and rejecting boundary samples to form a balanced
data set; and training and selecting an optimal classifier to construct a prediction model, and outputting a prediction category and probability. According to the method, data distribution is balanced through generated high-quality minority samples, the recognition capability of a classification model on
drug-induced autoimmune positive samples is effectively enhanced, the accuracy of drug immunotoxicity prediction is greatly improved, and immunotoxicity evaluation in the drug research and development process can be assisted.