FGCNSurv: dually fused graph convolutional network for multi-omics survival prediction

MOTIVATION: Survival analysis is an important tool for modelling time-to-event data, for example, to predict the survival time of patient after a cancer diagnosis or a certain treatment. While deep neural networks work well in standard prediction tasks, it is still unclear how to best utilize these deep models in survival analysis due to the difficulty of modelling right censored data, especially for multi-omics data. Although existing methods have shown the advantage of multi-omics …