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Preprints, Working Papers, ... Year : 2021

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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Dates and versions

hal-03224492 , version 1 (12-05-2021)

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Mathilde Sautreuil, Sarah Lemler, Paul-Henry P.-H. Cournède. Neural networks to predict survival from RNA-seq data in oncology. 2021. ⟨hal-03224492⟩
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