Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks

Background Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genome features and the contribution weights of different data-views in data integration. It is widely accepted that the regulatory associations between features play important roles in cancer subtype studies. In addition, different data-views may have different contributions in data integration for cancer subtype prediction.