  Cancer survival prediction is important for developing personalized
treatments and inducing disease-causing mechanisms. Multi-omics data
integration is attracting widespread interest in cancer research for providing
information for understanding cancer progression at multiple genetic levels.
Many works, however, are limited because of the high dimensionality and
heterogeneity of multi-omics data. In this paper, we propose a novel method to
integrate multi-omics data for cancer survival prediction, called Stacked
AutoEncoder-based Survival Prediction Neural Network (SAEsurv-net). In the
cancer survival prediction for TCGA cases, SAEsurv-net addresses the curse of
dimensionality with a two-stage dimensionality reduction strategy and handles
multi-omics heterogeneity with a stacked autoencoder model. The two-stage
dimensionality reduction strategy achieves a balance between computation
complexity and information exploiting. The stacked autoencoder model removes
most heterogeneities such as data's type and size in the first group of
autoencoders, and integrates multiple omics data in the second autoencoder. The
experiments show that SAEsurv-net outperforms models based on a single type of
data as well as other state-of-the-art methods.
