WEmarker: breast cancer-specific prognostic analysis with weighted multiplex network embedding

In clinical trials, prognostic biomarkers have become essential for guiding treatment decisions after breast cancer surgery. Network-based methods have gained notable attention to reveal marker genes, but many existing methods only focus on a single network, which inevitably neglects the incompleteness of interaction relationships within the network. Even when based upon the multiplex network, most of methods directly integrate the multiplex network into an aggregated network and do not take into account the inherent noise in the biological networks, which can not preserve the topological structure of each original network very well. In this study, we propose a novel method, WEmarker, for breast cancer-specific prognostic analysis. WEmarker reduces the noise level of biological networks and quantifies the probability of interactions between genes, and represents the nodes in the weighted multiplex network as vectors while efficiently retaining the structure information of these networks for identifying prognostic biomarkers. The results show that WEmarker outperforms comparative methods and the case study also demonstrates that biomarkers identified by WEmarker have reliable biological interpretability for breast cancer prognosis.