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

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.
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Preprints, Working Papers, ...
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Contributor : Mathilde Sautreuil Connect in order to contact the contributor
Submitted on : Wednesday, May 12, 2021 - 11:21:12 AM
Last modification on : Monday, December 13, 2021 - 9:17:14 AM
Long-term archiving on: : Friday, August 13, 2021 - 6:04:24 PM


  • HAL Id : hal-03224492, version 1
  • ARXIV : 2105.08338


Mathilde Sautreuil, Sarah Lemler, Paul-Henry Cournède. Neural networks to predict survival from RNA-seq data in oncology. {date}. ⟨hal-03224492⟩



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