Abstract: Objective To construct a breast cancer gene-drug network model for extracting and predicting the correlations
between breast cancer-related genes and drugs. Methods We developed an algorithm based on the ABC principle and the
association rules to obtain the correlations between the biological entities. For breast cancer, we constructed 3 different
correlations (gene-gene, drug-drug and gene-drug) and used the R language to implement the associated network model. The
reliability of the algorithm was verified by ROC curve. Results We identified 185 breast cancer-associated genes and 98
associations between them, 97 drugs and 170 associations between them. The breast cancer genes-drugs network contained 127
genes and 77 drugs with 384 associations between them. Conclusion We identified a large number of different correlations
between the breast cancer-related genes and drugs and close correlations between some biological entity pairs that have not
yet been reported, which may provide a new strategy for experimental design for testing personalized breast cancer treatment.