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.

Investigating a Breast Cancer Gene Expression Data Using a Novel Clustering Approach Historically, breast cancer has been perceived as a disease with varying histological and clinical features. Breast cancer tumor classification is important in disease prognosis and prediction because different breast tumors respond differently to different treatments and have different survival rates. Gene expression profiling studies have increasingly been motivated in the past decades to develop a good classification of breast cancer in molecular subtypes, which can improve the standard clinical assessments by providing extra prognostic information. In this research, one of the most comprehensive breast cancer gene expression datasets is analyzed by applying a novel clustering approach to predict the breast cancer subtypes. The novel unsupervised clustering approach initially model the gene expression data as a network and employ a community detection method to identify network clusters. This method utilizes an efficient problem specific metaheuristic algorithm to optimize the modularity value and identify clusters of breast cancer samples with similar characteristics that presents different subtypes of breast cancer. To assess the significant of the newly defined breast cancer subtypes, we compared our findings with three breast cancer subtyping methods.