  Survival prediction is an important branch of cancer prognosis analysis. The
model that predicts survival risk through TCGA genomics data can discover genes
related to cancer and provide diagnosis and treatment recommendations based on
patient characteristics. We found that deep learning models based on Cox
proportional hazards often suffer from overfitting when dealing with
high-throughput data. Moreover, we found that as the number of network layers
increases, the experimental results will not get better, and network
degradation will occur. Based on this problem, we propose a new framework based
on Deep Residual Learning. Combine the ideas of Cox proportional hazards and
Residual. And name it ResSurv. First, ResSurv is a feed-forward deep learning
network stacked by multiple basic ResNet Blocks. In each ResNet Block, we add a
Normalization Layer to prevent gradient disappearance and gradient explosion.
Secondly, for the loss function of the neural network, we inherited the Cox
proportional hazards methods, applied the semi-parametric of the CPH model to
the neural network, combined with the partial likelihood model, established the
loss function, and performed backpropagation and gradient update. Finally, we
compared ResSurv networks of different depths and found that we can effectively
extract high-dimensional features. Ablation experiments and comparative
experiments prove that our model has reached SOTA(state of the art) in the
field of deep learning, and our network can effectively extract deep
information.
