  Survival analysis consists of studying the elapsed time until an event of
interest, such as the death or recovery of a patient in medical studies. This
work explores the potential of neural networks in survival analysis from
clinical and RNA-seq data. If the neural network approach is not recent in
survival analysis, methods were classically considered for low-dimensional
input data. But with the emergence of high-throughput sequencing data, the
number of covariates of interest has become very large, with new statistical
issues to consider. We present and test a few recent neural network approaches
for survival analysis adapted to high-dimensional inputs.
