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Ensemble Classifier based on gene synergistic network improves breast cancer outcome prediction

Stable prediction of prognosis is significant for better individualized treatment of patients with breast cancer. Different methods have been proposed to predict the prognosis of breast cancer patients. Traditional classifiers were mostly based on gene signatures, which were less practical due to their poor stability. Recently, a classifier based on deep learning improved this problem, but there were still other shortcomings. In this paper, we proposed the classifier based on gene synergistic network. Firstly, we considered the fact that one single gene had no effect on the clinical outcome in breast cancer patients but that two genes may work together to affect the clinical outcome, and we used the uncertainty coefficient to measure the relationship between the two gene and the clinical outcome. Based on three kinds of synergistic relationships, we constructed three gene synergistic networks. Then each community in the networks was used to establish a weak classifier. Finally, using majority voting strategy, the weak classifiers with great classification ability were integrated into an ensembled classifier. The predicted results of different data sets showed our classifier was superior to the traditional classifiers and had great stability. In addition, the result of function annotation showed that the genes involved in our classifier were of biological significance and related to cancer, indicating that the gene synergistic networks were meaningful. In conclusion, a community in gene synergistic network can reveal a specific biological process in the prognosis of breast cancer patients, and several communities can contribute to studying biological mechanism in cancer metastasis, thus the ensemble classifier based on gene synergistic network performed well in both accuracy and stability.