Neural networks to predict survival from RNA-seq data in oncology

TL;DR

This paper investigates the application of neural networks to predict patient survival times using high-dimensional RNA-seq data, addressing challenges posed by large covariate spaces in oncology.

Contribution

It introduces and tests recent neural network methods specifically adapted for high-dimensional survival analysis with RNA-seq data.

Findings

Neural networks can effectively handle high-dimensional survival data.

The study demonstrates the potential of neural networks in oncology prognosis.

Neural network approaches outperform traditional methods in this context.

Abstract

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.