Comprehensive characterization and identification of cancer subtypes have a number of applications and implications in life science and cancer research. Technologies centered on the integration of omics data hold great promise in this endeavor. This paper proposed a multiplex network-based approach for integrative analysis of heterogeneous omics data. It represents a useful alternative network-based solution to the problem and a significant step forward to the methods in which each type of data is treated independently. It has been tested on the identification of the subtypes of glioblastoma multiforme and breast invasive carcinoma from three omics data. The results obtained have shown that it has achieved the performance comparable to state-of-the-art techniques (Normalized Mutual Information >0.8). In comparison to traditional systems biology tools, the proposed methodology has several significant advantages. It has the ability to correlate and integrate multiple data levels in a holistic manner which may be useful to facilitate our understanding of the pathogenesis of diseases and to capture the heterogeneity of biological processes and the complexity of phenotypes.
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