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

Gang Wen, Limin Li

Abstract: Survival analysis is an important tool for modeling time-to-event data, e.g. 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 modeling right censored data, especially for multi-omics data. Although existing methods have shown the advantage of multi-omics integration in survival prediction, it remains challenging to extract complementary information from different omics and improve the prediction accuracy.