Breast cancer is one of the most common malignant cancers among women with increasing number of patients. Gene regulatory network and identifying target genes for cancer treatment, and reducing breast cancer death rates is of great importance medically. This study aims to model gene regulatory network of breast cancer using hidden Markov model which greatly aids doctors in early diagnosis and faster treatment of breast cancer using identification of target genes. In this study, gene expressions of 206 patients diagnosed with four subtypes of breast cancer including, Basal, Her2, LumA, LumB, were obtained from the Cancer Genome Atlas (TCGA). 8 genes with the verified interaction among them were investigated by hidden Markov model of gene regulatory network and target genes. with the results of transition probability matrix, FADD, TNFRSF10B, CASP8 are the target genes in the mentioned cancer subtypes so that genes that their transmit probabilities are more than an initial value of 0:125 are regulatory genes and transmit matrix identifies the probability of the mentioned cancers regarding gene expression level
